tag:blogger.com,1999:blog-60599833103256782832024-02-22T16:10:44.039+00:00The Power of Goals.Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.comBlogger579125tag:blogger.com,1999:blog-6059983310325678283.post-5771524265950710202023-07-29T09:25:00.009+01:002023-07-29T09:40:11.114+01:00The Missing Ingredient<p>Ages ago, Opta used to have an OptaPro blog (I wrote the first article).</p> <p>I also
wrote a blog about trying to utilise how close (or not) off target goal attempts
came to requiring a save.</p> <p> It was called something like "Don't be afraid to miss"
and centred around data relating to Robin van Persie. (It was that long ago).</p>
<p>The site has long gone, but with access to more extensive data, such as shot
placement (including off target attempts), I've revisited the idea to see if the
intuition that "good finishers", when they miss, don't miss by much, is valid or
not.</p> <p>The idea is fairly basic. A shot that hits the post, is inches away from
being a high quality post shot xG, whereas one that flies high and wide is going
to need a fair bit of resighting to trouble the keeper. </p> <p>The metric will also be
related to the situation from which the attempt originated (open play, free
kicks- etc), where on the field it came from (six yard box, outside the box) and
whether the head or the boot was used.</p> So I took every off target non penalty
attempt from the big five league for the last three completed seasons (over
53,000) and modelled by how far a typical big five player missed the target with
their wayward efforts based around these pre-shot variables.</p> <p>I then compared the
"expected waywardness" to the actual waywardness of individual players for every
play type scenario.</p> <p> I expected Messi to come top. He didn't. He came second out
of over 3200 players, although he did have three times as many errant efforts
than the player who beat him (Matteo Politano). So Messi's number are more
robust. </p> <p> Here's the top ten players whose off target attempts are close enough to
elicit an "Ohh" from the crowd, along with the ten players whose misses gave the
goalframe the widest berth.</p>
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<p>The lists seem to pass the eye test. Messi, de Bruyne and Son in one list and
Maupay and Havertz in the other. </p> <p>I took the 30 top post shavers and looked at
their NPxG compared to their actual goals and it was a cumulative 489 NPxG
compared to 556 actual goals, an over-performance of 13.7%.</p> <p> The worst 30
hopelessly wayward had a cumulative NPxG of 434.9, but just 394 actual goals
scored. An under-performance of 9.4%.</p> <p> Roughly 40% of goal attempts are retrieved
by the ball boy/girl. But rather than discarding that sizeable chunk of data,
there might be good reason to at last try to gather some insight from these
wayward efforts.</p>
Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-49164857122403021172023-06-27T19:21:00.001+01:002023-06-28T07:39:28.943+01:00The Ageing of the Ageing CurveA long, long, long, long time ago (<a href="http://thepowerofgoals.blogspot.com/2013/03/ryan-giggs-very-nearly-40-not-out.html">2013</a>) there wasn't that much granular data around and certainly hardly any based around metrics that eventually led to xG entering the mainstream of football/soccer analytics.
Therefore, proxies abounded and share of playing time to evaluate a player's ageing rise and fall quickly became a go to method. Pull enough player data for minutes played, trawl through wiki for dob's and you came up with a pleasing curve that rose from the teenage years to the early twenties, peaked in the mid to late 20's and fell away as the ex pro went away to impart gnarled cliches on Sky.
Plot the season by season change in playing time, the delta method & the linear trendline cut the axis where it was assumed that effortless push came to more laboured shove.
<div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh6KyOO53EUVq4WzAEXPHW63wBTklW5f8TczhAMC_YSNCZIT6fuiQuUD9U3rl7hsjQUGJKbAgBxp8b1X2yeMr6APriyKKZJnlgWXW4f44UVjnHnvKE5mDdQBfxKU1lFrWzBBSwzbMBj1vXCiuZqP6NVTtG98scYhnmVICr4rneAjUiHihv1teVhH_esMVjP/s888/save.png" style="display: block; padding: 1em 0; text-align: center; "><img alt="" border="0" width="320" data-original-height="677" data-original-width="888" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh6KyOO53EUVq4WzAEXPHW63wBTklW5f8TczhAMC_YSNCZIT6fuiQuUD9U3rl7hsjQUGJKbAgBxp8b1X2yeMr6APriyKKZJnlgWXW4f44UVjnHnvKE5mDdQBfxKU1lFrWzBBSwzbMBj1vXCiuZqP6NVTtG98scYhnmVICr4rneAjUiHihv1teVhH_esMVjP/s320/save.png"/></a></div>
When a player's minutes finally fell instead of maintaining an ever shallower upward trend, the assumption was that he had become less effective on the field, performance levels had fallen, the manager had taken note and action had ensued.
Physical decline, it was assumed, had begun to outstrip experience and smarts.
We're now over a decade further down the road to analytical enlightenment, where on and off ball stuff gets routinely measured, even if there's still no consistency in naming metrics. Creativity, shooting execution & positional sense, ball progression via passes or carries & risk/reward has become less blurry & more transparent.
We've also seen advances in sports science to prolong a the peak age of performance & witnessed anecdotal evidence for increased player longevity, even in demanding roles.
So it's well overdue to usher "share of minutes played" into the lobby, treat it as just a fraction of what happens along the age curve & try to understand what might be going on in a player's career arc.
Non shot expected goals added was one powerful metric that sought to measure by how much individual ball progression improved a team's likelihood of scoring. The delta approach to non shot xG added per 90 shows a gradual increase in performancein the early years of a player's career, but thereafter there's virtually no change, on average in the performance levels achieved per 90 minutes.
It's not quite an expected trendline, more early improvement, but then flatlining.
<div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhD1xOJsDqBiQtl_lxZBYZERfFmtPGlz7wPHndYTspzntLgvFk-l9xYK1aYS7CK88fNOVCHs0DATjFsth0ujMnd4REIB8tNJj-5NbUqv1vGMLQQI-izqT2mA3DiEH-bIHGHHWLhb68vempyuebSI18zrS1cEAzDlwBJoCkCw0TFdDdDTJ8Qpw1f7wxExFyh/s712/ns1.png" style="display: block; padding: 1em 0; text-align: center; "><img alt="" border="0" width="320" data-original-height="428" data-original-width="712" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhD1xOJsDqBiQtl_lxZBYZERfFmtPGlz7wPHndYTspzntLgvFk-l9xYK1aYS7CK88fNOVCHs0DATjFsth0ujMnd4REIB8tNJj-5NbUqv1vGMLQQI-izqT2mA3DiEH-bIHGHHWLhb68vempyuebSI18zrS1cEAzDlwBJoCkCw0TFdDdDTJ8Qpw1f7wxExFyh/s320/ns1.png"/></a></div>
The ball progression illustrated combines passes & carries and it's generally accepted that the latter is more physically demanding than the former. Perhaps players are replacing any shortfall of xG added via carries by upping their output from passes.
To see what may be happening I looked at how NS xG added from just carries has changed over the last three completed seasons for all players as they age and again there's virtually no change on average in the rate of xG added from carries as a player ticks off their birthdays.
<div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWKC-RAEHojFbzZfYooGIKVyRgLxg7V7VWzZRJtBmQX93DnbDDn549m5duGVy2STMn8Mj2b4sqhsN0XQQ-7ZGJgckjy4f6YGep7dvJcs0YMwJufGyc_1tJk0uqZ-3s05odhKRDhZqGQDCEJcXbZcUhhIlv_u-1vCVJ-L7nazFH0Cs53C3b6sIUqPFiZHUU/s853/ns2.png" style="display: block; padding: 1em 0; text-align: center; "><img alt="" border="0" width="320" data-original-height="494" data-original-width="853" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWKC-RAEHojFbzZfYooGIKVyRgLxg7V7VWzZRJtBmQX93DnbDDn549m5duGVy2STMn8Mj2b4sqhsN0XQQ-7ZGJgckjy4f6YGep7dvJcs0YMwJufGyc_1tJk0uqZ-3s05odhKRDhZqGQDCEJcXbZcUhhIlv_u-1vCVJ-L7nazFH0Cs53C3b6sIUqPFiZHUU/s320/ns2.png"/></a></div>
Persuasive speculation that as a player ages, their performance levels dip & they get left out of the side more frequently, kept "share of minutes played" a respected metric for nearly a decade. But how the new quantifiable metrics <i>don't</i> change that much well into a player's 30's may suggest that there is a slightly different dynamic at play for individuals, overall.
Namely, player metrics, even more physically demanding ones which involve ball carrying, can with good managing of playing time enable players past what was considered their prime to maintain their own high standards.
In short, you might get very similar levels of performance in a player's early 30's as you got in their mid 20's.....just not quite as often as you did previously.
Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-2440962046638902262021-11-14T11:34:00.000+00:002021-11-14T11:34:37.204+00:00Football Analytics' Big Own GoalJust a small vent regarding what a poor job the early analytics community did and continue to do when naming metrics.
I know it's been widely pointed out, but "expected goals" is an awful name for the premier metric and nothing screams elitism and jargon than almost always going to acronyms.
xG, PSxG, xA, NSxG may be easily understood by anyone who has immersed themselves in the topic, but as someone who has tried to get these ideas to a wider, more general football obsessed audience, they are an immediate barrier.
It's almost certainly too late to begin using titles that use everyday language *and* are self explanatory, chance quality, for example rather than expected goals or on target chance quality, rather than post shot expected goals, (which isn't even accurate!).
If you need a glossary to write an article about a team or player, who've failed. If you need to write "expected goals, which is"........... The same.
Numerical values and decimal places are usually enough to disengage otherwise passionate fans of a sport. Chuck in jargon and you're almost inviting a negative reaction, regardless of the points you are trying to highlight.
Three rules of naming metrics.
1) Don't use acronyms.
2) Use familiar language, ideally associated with the sport.
3) DUA
Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-8023352458392242122021-05-28T10:15:00.000+01:002021-05-28T10:15:06.252+01:00What is Goal Expectation?<div>Let's say you want to make an informed estimation about the upcoming England vs Scotland game at Wembley Stadium in Euro 2020 (2021).</div><div><br /></div><div>One route would involve estimating the average number of goals England are likely to score against Scotland at Wembley and the average number of goals Scotland would score against England at the same venue.</div><div><br /></div><div>You could then take a mathematical route to calculate the probability that two side with these average goal expectation estimates would result in a home win, away win or a draw. </div><div><br /></div><div>Typically a Poisson approach.</div><div><br /></div><div>The average number of goals expected to be scored or allowed by a side in a future game has for over 30 years been referred to as their <b>goal expectation</b>. </div><div><br /></div><div>Unfortunately, a more recent and widely discussed metric based on the chance quality of a scoring opportunity, has arrived on the scene and taken the very similar name of <b>expected goals</b>.</div><div><br /></div><div>They are not the same.</div><div><br /></div><div>The former, <b>GOAL EXPECTATION,</b> is a measure of the likelihood of success for a side <b>prior</b> to kick off, based on historical data that is used to quantify the difference in quality between the sides. (It may even use historical expected goals data).</div><div><br /></div><div>The latter, <b>EXPECTED GOALS</b>, is a value ascribed to the quality of attempts on goal, <b>after the fact</b>, based on the characteristics, shot type, location etc of each attempt.</div><div><br /></div><div>The <b>goal expectation</b> of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively.</div><div><br /></div><div>The <b>expected goals</b> for the game hasn't yet materialised.</div><div><br /></div><div><br /></div><div><br /></div><div><br /></div>Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-77900012899618493142021-03-12T13:01:00.000+00:002021-03-12T13:01:10.737+00:00XG as Easy as 1,2,3<p>One of the
more interesting variants in the expected goals evolutionary backwater broke
the scoring process down into stages. Most models go directly from shot
location to goal/no goal output, but it is possible to include each of the
possible outcomes.</p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">A goal
needs to jump through a variety of hoops to register (VAR excluded).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Shots can
be blocked, they can miss the target, they can hit the woodwork or the can be
saved before they enter the record books and each of these possibilities can be
modelled separately.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">This route
isn’t inherently better than a single stage model, but it does help to throw a
more descriptive, if not necessarily predictive light onto why and how a player
is excelling or failing to convert location based chance quality into outcome
based success.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">It has been
useful in trying to unpick the Brighton conundrum.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">A plethora
of underperformance has seen more blocks than expected from shots taken by
Brighton players compared to an “expected blocks” model. This is further
enhanced by the distance between blocker and Brighton shooter being the lowest
in the league, they are getting closed down more extensively than any other
team.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Which may
suggest a slow and labored build up is degrading Brighton’s xG chances beyond
what may be picked up by a one stop, rather than multi-layered xG model.
Attacking tweaks, rather than patiently waiting for regression to kick in may
be needed.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The next
stage in the progression from shot to potential goal involves getting the ball
on target.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">One of the
first xG think pieces I wrote for the now defunct OptaPro blog suggested that
getting the ball on target wasn’t quite as straightforward a metric as it first
appeared. In short, getting lots of shot on target wasn’t always the sign of an
above average striker.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Robin van
Persie, then of Manchester United was the guinea pig and his rather less than
impressive rate of working the keeper with on target attempts didn’t seem to
hurt his scoring performance. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The
solution I suggested was that some players who aimed for more difficult to save
areas of the goal, top corner, for example, might miss more frequently than
players who prioritized target hitting at the expense of save difficulty.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In short,
strikers shouldn’t be afraid to miss the goal.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">So, we’ve run
through two of the three xG stages. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Don’t get
your shot blocked (that seems a universal aim, there seems a limited benefit in
taking the ball so close to a blocking defender that the chances of having the
shot blocked increases greatly).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Hit the
target. A more ambiguous ambition. Most strikers could hit the target most of
the time, but might compromise the difficulty to save their goal bound attempt.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The final
stage is more akin to the traditional, one step model, but instead attempts
that successfully negotiate the initial two stages are modelled against out of
sample goal/no goal outcomes.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We’ve now
got a multi-step xG model (that didn’t catch on from 2014), that adds tons of
missing context that can be used to explain the “how” of why a player is
returning the outcome from a location based process, even if it still falls to
good old random variation to explain away much of the future performance
levels.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Some
factors affecting xG output may be systematic to teams or players (randomness is
still the major player?) and by breaking the process down stage by stage, you can
perhaps shine a light onto these additional factors.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Finally,
here’s how over and under performers, with at least 10 regular play goals from
shots only have maneuvered their way through the three stages of xG since
2016/17.<o:p></o:p></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-_AEmedh9GhV9XNioFo_Bf7rHs5Pfj8QlJNdAOEEIQhQthokx6tLuWkJ6Wl7uc2jFH4HaPm_Y5mdg-UAj1OBcWwQhteBY-xdB8TiJP3ozbRcmMqVscCZbFPJ2pn3BYwuXP2ItvkQgNE2e/s934/3stage.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="934" data-original-width="622" height="792" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-_AEmedh9GhV9XNioFo_Bf7rHs5Pfj8QlJNdAOEEIQhQthokx6tLuWkJ6Wl7uc2jFH4HaPm_Y5mdg-UAj1OBcWwQhteBY-xdB8TiJP3ozbRcmMqVscCZbFPJ2pn3BYwuXP2ItvkQgNE2e/w527-h792/3stage.png" width="527" /></a></div><br /><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span><p></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The table
above includes diverse shooting profiles, which may be useful as a descriptor
or potential as a coaching aid if the multi-stage xG model can pick up
systematic flaws or talents that persist.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Jimenez
avoids blocks at a league average, but then misses the target wantonly and his
overall scoring from regular play with his boot falls way below the average
expectation.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Grealish
has more shots blocked than expected, misses the target more frequently, but runs
a large over performance for goals scored. Placement is the likely culprit,
here.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Whereas,
Wood avoids blocks, hits the target, but tamely refuses to accumulate above
average goal tallies.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">It’s time
to take data to the video booth.<o:p></o:p></span></p><br /><p></p>Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-30760598322963231902020-12-24T09:30:00.004+00:002020-12-24T09:30:52.850+00:00Stoke and the Art of Crossing
<p style="margin: 0px 0px 10.66px;"><u><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Stoke
Highlight the Art of Crossing.</span></span></u></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Two Stoke
City games, two headers, two goals and a duo of 1-0 wins not only demonstrates
the fine lines that can separate six points from two in a low scoring sport,
such as football, but also the important role still played by crosses in the
modern game.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Lavishly
assembled squads may partly spurn crossing as a primary route to goal in favour
of more intricate, possession based passing sequences to create space before
the final delivery, but even the likes of Arsenal when faced with the need for
a goal do fall back on the traditional cross.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">33 crosses
yielded a single goal in a recent 2-1 home defeat for Arteta’s side against
Wolves and infamously, Manchester United attempted over 80 crosses in a drawn
game with Fulham in the last days of David Moyes’ reign.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Crossing,
as a primary strategy reached a low point with Liverpool’s 2011/12 team
consisting of a big target man, Andy Carroll and a host of players ready to
deliver a cross, led by Stewart Downing.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Unfortunately,
such a predictable game plan & and tendency to cross the ball early from
less advanced field positions, resulted in a failed experiment. An average of
21 Liverpool crosses per game was rewarded with just four Premier League goals.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Present day
Liverpool lead the analytics revolution, but their failed, decade old legacy
helped to kick start that revolution, as data was used to explain why their
cross heavy approach failed and where the lesson lay for teams to maximize the
returns from a wide player’s staple delivery.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Crosses in general
are inefficient. </span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Leagues
vary, but as a baseline number, it takes upwards of 90 crosses to score a goal
directly from the delivery. Secondary chances created after the initial header
or shot, but during the same phase of play, improves the strike rate to around
one goal every 50 crossed balls.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">However,
not all crosses are equal. The danger is more apparent if a side works a
delivery from the byline compared to a last-minute desperation hoof from deep into
the mixer.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Fortunately,
data can differentiate between types of crosses. Whether the ball was chipped
or driven on the ground, for example. But where crosses originate and where
they are aimed provides the biggest insight into how to turn a cross into a
winning formula.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">You can
divide the origin and intended destination of a cross into two broad categories
depending on how effective they are at producing goals.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">In the
graphic below, prime areas are shown in red and the least effective in blue.</span></span></p><p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;"><br /></span></span></p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2rNZWAxuH6dbITiElN5pEVnEFF9LsX2xsGCu8NBWbAT6p8JiTxLBgML8KD0oSZKku1AvYiuYHyqvyPSywK2YJCSaFRdKOJkc4bArk4hffPS4Ltk11Jb0svyOJOphwNOqbEwH54aobwoDx/s1105/crosssssss.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="827" data-original-width="1105" height="362" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2rNZWAxuH6dbITiElN5pEVnEFF9LsX2xsGCu8NBWbAT6p8JiTxLBgML8KD0oSZKku1AvYiuYHyqvyPSywK2YJCSaFRdKOJkc4bArk4hffPS4Ltk11Jb0svyOJOphwNOqbEwH54aobwoDx/w485-h362/crosssssss.png" width="485" /></a></div><br /><p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Blue
wasteful target areas are intuitive. </span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">If the ball
is aimed too close to the goal line, they become prey to a dominant keeper. But
place the cross too close to the edge of the box and any shot or header will be
taken from distance and for every yard a striker moved away from the goal, the
likelihood of a goal falls by ten percent.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">The red
sweet spot is between these two areas.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">The
touchline hugging, wasteful blue delivery areas give both the keeper and
defenders time to defend the box, whereas moving infield to deliver the cross reduces
defensive reaction time and greatly improves conversion rates.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Hitting a
ball from a wide and deep wing position to the wasteful area of the six-yard
box, going from one blue zone to another, only produces a goal every 500
attempts. Whereas a delivery from a red, prime infield area to a red, prime
area of the box increases conversion rates to around one goal every 20 crosses.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Stoke
City’s two winning goals against Wycombe and Middlesbrough have been added to
the graphic and hit the sweet spot for both Fox & McClean’s delivery and
Collins & Powell’s headed goals. They were assists that were drawn from the
most productive area of the crossing playbook.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Of course,
there’s much more than “crossing by the numbers” to a successful outcome. </span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Powell is
an accomplished header of the ball. During his Championship career over 20% of
his goal attempts have been from headers and he is adept at getting on the end
of higher quality attempts than the league average. Whilst Collins’ physical
attributes are obvious.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Campbell
then crossed from one prime area to another for Cardiff to obligingly smack the
ball into their own net, before he departed on a season long, injury induced
hiatus, Fox hit the prime red zone with a pacy cross to defeat Blackburn &
Brown repeated the prime to prime connection to set up Thompson to briefly draw
level with Spurs in the Carabao Cup 1/4 final. <span style="margin: 0px;"> </span><span style="margin: 0px;"> </span></span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Clever off
the ball running also contributes, a seen by Vokes drawing away Wycombe
defenders with his near post run & Stoke creating an over load of far post
attackers for the goal against Middlesbrough.</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">Over recent
games, Stoke City had the crossing basics in place and good things followed,</span></span></p>
<p style="margin: 0px 0px 10.66px;"><span lang="EN-US" style="margin: 0px;"><span style="font-family: calibri;">On the
weekend when Stoke climbed into the playoff spots on the back of two smartly
executed crosses, Arsenal in the North London derby were again trusting more to
luck by throwing in another 44 crosses in the vain pursuit of a goal.</span></span></p>
Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-46375058200838931192020-04-20T09:03:00.000+01:002020-04-20T09:14:30.514+01:00Scatter PlotsThere's been a huge increase in football related scatter plots recently. So as the guy who produced the first such plots, I thought I'd quickly run through why I thought this simple plot was useful and then try to expand the idea to provide additional usefulness.<br />
<br />
The initial plots were designed to both inform and characterise playing style.<br />
<br />
I think still the most successful plots use related metrics, for example expected assists and expected goals per 90 for individual players.<br />
<br />
These "makers and takers" plots easily split players into those whose predominant talent is to create chances, those who get onto the end of opportunities and those rare players who excel at both disciplines.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm5O01Dlhfw-UvetfPjmbpqV0IFOXl49wy1-CRFMONyy-Mq6bx8YOwtyfnuggtRXOqNcCRBGSqtzROlvr-3f2TOl09A8i0Xxm_yXnohLZm6bx7l2We8qCv6d6ZVECGQRss05TlFaL82KCk/s1600/11.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="472" data-original-width="716" height="420" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm5O01Dlhfw-UvetfPjmbpqV0IFOXl49wy1-CRFMONyy-Mq6bx8YOwtyfnuggtRXOqNcCRBGSqtzROlvr-3f2TOl09A8i0Xxm_yXnohLZm6bx7l2We8qCv6d6ZVECGQRss05TlFaL82KCk/s640/11.png" width="640" /></a></div>
Here's one for Arsenal 2019/20.<br />
<br />
It's got sample size issues, but it's fairly evident that the creative players are towards the top left and the goal poachers are to be found in the bottom right.<br />
<br />
Another quite neat aspect of this type of plot is that you can run a line through a player to the origin and any one with a similar ratio of xG and xA will lie close to that line.<br />
<br />
In league wide samples, therefore you can find emerging players with similar qualities to the established stars.<br />
<br />
There's a lot of data swilling around today, these plots are simple to make, three minutes tops, and with some thought about what you're trying to illustrate, they inform pretty well.<br />
<br />
Over the weekend I came back to the idea, to see if I could add information that tells you a little bit more than just the raw connection between two metrics.<br />
<br />
Here's what I came up with. It's again just a simple scatter plot, but I've used bubble size to introduce a third variable (metric volume per 90). <br />
<br />
In addition I've used a single performance metric (NS xG added from ball carries) along the x axis and instead of plotting a complementary metric on the vertical axis, I've used a number to denote how diverse the x axis metrics are for each player.<br />
<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPkE2O0OKoh68fiL2Prxxn4MUlg8OMDkI9jH7Ylyb8wTXKOVrgbPfyaXPFn-6cBfwR1PyZUTcp6-lZayyEES1aYXkOkkgEdi5L9nu3QhpvG4YVXB46JiOZy7Q4dZGb5tiL8bkCd_SC2hH8/s1600/carryentropy.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="663" data-original-width="1101" height="384" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPkE2O0OKoh68fiL2Prxxn4MUlg8OMDkI9jH7Ylyb8wTXKOVrgbPfyaXPFn-6cBfwR1PyZUTcp6-lZayyEES1aYXkOkkgEdi5L9nu3QhpvG4YVXB46JiOZy7Q4dZGb5tiL8bkCd_SC2hH8/s640/carryentropy.png" width="640" /></a></div>
<br />
This just plots the top 20 NS xG added by players through their ability to successfully carry the ball forward and move their team into a more dangerous pitch position.<br />
<br />
It's a good one to chose because you know that Adama Traore will top the list (and he does).<br />
<br />
Rather than a sterile scatter, you've now got a chart that not only tells you about a performance metric, it also instantly adds another layer (success volume) from which you can draw addition information about the characteristics of a player.<br />
<br />
In short, those towards the right of the plot add more NS xG per 90 than others.<br />
Larger bubble size indicates more successful progressive carries per 90.<br />
And higher up the chart indicates more disorder and unpredictability in what a player will positively achieve for his team when on the all.<br />
<br />
I've annotated players with the additional information you can draw from these plots.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-72147991859620037182019-12-26T10:54:00.001+00:002019-12-26T11:00:41.699+00:00State of Play 2020<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Liverpool’s
bilingual mastermind behind the team’s meteoric rise to dominate club, domestic,
European and now world football is gradually gaining a higher media profile.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Not Jurgen
Klopp, although he has played a part in the Red’s success, but Dr Ian Graham,
their current director of research.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Ian’s
recent appearances in both the spoken and written media has not only
highlighted the importance of an integrated approach to squad building that
utilizes a data driven approach, alongside more traditional methods, it has
also given a small glimpse into the analytical methods employed.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span style="font-family: "calibri";"><span style="font-size: large;"><span lang="EN-US" style="margin: 0px;">The latest
profile landed courtesy of </span><a href="https://www.liverpool.com/liverpool-fc-news/transfer-news/liverpool-transfer-recruitment-ian-graham-17458056"><span lang="EN-US" style="margin: 0px;"><span style="color: #0563c1;">Liverpool.com</span></span></a><span lang="EN-US" style="margin: 0px;"> and described some fundamentals of
Liverpool’s analytical philosophy.</span></span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">One
particularly resonated with <a href="https://www.infogol.net/en"><span style="color: #0563c1;">Infogol’s</span></a>
approach of quantifying every footballing action in the same currency of goals
or more specifically x goals.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">The idea
that every action, be it a pass, tackle or long throw changes the likelihood
that a side will ultimately score isn’t a new concept.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">It was
probably first introduced into the public analytical domain by <a href="https://twitter.com/smarterscout"><span style="color: #0563c1;">Dan Altman</span></a> in his whistle stop
OptaPro presentation in 2015 and hints of such models have been recently
emerging from <a href="https://www.optasportspro.com/"><span style="color: #0563c1;">Opta</span></a> itself and <a href="https://twelve.football/"><span style="color: #0563c1;">Twelve football</span></a>. </span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Such a
non-shot xG model also powers Infogol’s “Team of the Week”.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">The gradual
migration, at least inside the industry, from a purely chance based evaluation
to a more holistic one somewhat mirrors the earlier transition from merely
counting shots, as exemplified by total shot ratios from 2008 to a more
informative, location based xG model, subsequently.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">However,
creating such non-shot models that quantify every on-field action is not a
simple task. The granular data required to build non-shot models dwarfs that
that was needed to create TSR, which itself was rudimentary and basic compared
to that required to create a proficient xG model.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">These leaps
in data driven evaluation presents a dilemma for the aspirations of public and
hobbyist analysts, an area that provided much of the driving force behind the early
explosion in football analytics.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Latterly,
monetization of ideas and a larger appetite for quantitative metrics to
supplement opinion driven insight in the media and clubs, has swept many of
those same hobbyists behind a non-disclosure paywall.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Less
co-operation, dwindling numbers, availability of adequate data and the need for
diverse technical skills to process that raw data, appears to have stifled the
growth of football metrics in the purely public arena.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">At the risk
of falling victim to one of Twitter’s sloganized insults, “back in the day,
metrics didn’t last long before they were improved upon or supplanted
altogether”.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Liverpool.com
suggested that Ian’s weapons grade model might be broadly replicated by current,
readily available and much quoted metrics, such as xG Chain (I’ll let you
google the definition).</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Succinctly,
the metric rewards every participant in a move that ends in a goal attempt with
that chance’s entire xG. </span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">The
distribution of goodies can seem churlish, for example, by giving far less
individual credit to the three Middlesbrough players who swept nearly the
length of Stoke’s defensive transition to score a low probability winner on
Friday night, as it would a marginally involved square ball on route to a
multiple passing move that ends with a tap in from six yards.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">More
crucially it completely omits actions that aren’t concluded by a created
chance.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">To test
Liverpool.com’s optimism, I compared Infogol’s non-shot ball progression via
passes and carries to the much-touted gold standard of xG Chain.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">To avoid
confusion over units, I’ve simply ranked the xG Chain and the non-shot ball
progression for each player in the recent Merseyside derby and then compared a
player’s rank in one metric with his rank in the other.</span></span><br />
<span style="font-family: "calibri"; font-size: large;"><br /></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjH8fUtqZz1wdt7gFbHPBB-Ybu_febb-hWT0Ha5J5Hs2mxN5qg4VtybAIvJtAyb9pft07FAbUNJmGbmqAjpbnvnS9ywXX1DxDHQDITsb76n8RE5qYWXFyAgnzY5zCzgZqHNPQxqaqOaSP4X/s1600/ian.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-size: large;"><img border="0" data-original-height="634" data-original-width="762" height="532" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjH8fUtqZz1wdt7gFbHPBB-Ybu_febb-hWT0Ha5J5Hs2mxN5qg4VtybAIvJtAyb9pft07FAbUNJmGbmqAjpbnvnS9ywXX1DxDHQDITsb76n8RE5qYWXFyAgnzY5zCzgZqHNPQxqaqOaSP4X/s640/ian.png" width="640" /></span></a></div>
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri";"><span style="font-size: large;"></span><br /></span></span></div>
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">It starts
off quite well. Sadio Mane ranks top in both, he was outstanding on the night.
But then, much like Stoke’s trip to Middlesborough, things take a turn for the
worse.</span></span><br />
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Shaqiri
ranked an impressive 2<sup><span style="font-size: xx-small;">nd</span></sup> overall in ball progression, but a lowly 16<sup><span style="font-size: xx-small;">th</span></sup>
in xG Chain, whereas Origi rates highly by the latter, but much less so in the
former.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Overall, a
third of the players have double digit ranking differences between their pecking
order in both metrics. There are some agreements, but the relationship between
the two metrics is generally weak.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Extend the
study to every game played last season and this tenuous correlation between the
two metrics remains.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">One of the
strengths of the early analytics movement was the ability to sift mere
statistical trivia (team Y has recorded X when player Z plays, immediately
springs to mind) from useful, if imperfect evaluations that convey insight and
can be used to both evaluate and project future performance.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">A great
example of the latter is <a href="https://twitter.com/DanKennett"><span style="color: #0563c1;">Dan Kennett’s</span></a>
recent Allisson tweet, which used big chances to highlight the keeper’s
importance to Liverpool, both in the past and possibly in the future. </span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Save rates
when faced with Opta’s Big Chances can be framed to be a very good proxy for a
more exhaustive and granular, post shot xG2 modelling of a keepers saves and
goals allowed.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">Dan’s tweet
was selective, but also carefully constructed enough to capture the keeper’s
core attributes. Current retweets are approaching around 10 billion!</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">That should
be the benchmark for widely used metrics and player contribution figures, such
as xG Chain fail that test on numerous counts. </span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">It fails to
differentiate individual contribution, omits larger swaths of creditable
actions and thus fails to correlate well with more exhaustive modelling of a
similar player process.</span></span></div>
<span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: "calibri"; font-size: large;">The
challenge for the public arena as we enter the roaring 20’s is to come up with
constant improvements to substandard and potentially misleading measures….. and
be more like Dan.</span></span></div>
<b></b><i></i><u></u><sub></sub><sup></sup><strike></strike><span style="font-family: "calibri";"></span>Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-37382552361876062992019-10-29T09:29:00.003+00:002019-10-29T09:32:46.153+00:00Liverpool by One.<br />
<div style="margin: 0px 0px 10.66px;">
<br /></div>
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Old style
goals based analysis hardly gets a run out nowadays with everyone arguing xG
strawmen. So, let’s go the goals route to see if Liverpool’s record in single
goal margin wins is “knowing how to win”, “unsustainable” or “about what you’d
expect”.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Liverpool
won 10 games by a single goal margin last season. That’s a lot, but well below
the single season record held by Manchester United of 16 in 2012/13 and 2008/09.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">United’s
number of single goal wins in those subsequent seasons fell to five and eight
respectively (although something more impactful may have also occurred in
2013/14). Their points tally fell as well, by 25 points in 2013/14 and by 5 in
2009/10.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">To dilute
the Fergie/Moyes effect, let’s look at the average record in the next season of
teams who won 10 or more games by a single margin. </span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">There’s
over 90 of them during the 20 team history of the Premier League and 80% of those
had fewer wins by the narrowest possible of margins during their next Premier
League season, 74% also saw their points total fall.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">These teams
who edged lots of close matches one season shed around 10% of their points in
the next season.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Initially,
it’s not looking too rosy for Liverpool’s ability to sustain these narrow wins.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">However,
there’s another factor to consider.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Single goal
wins, on average account for 41% of a side’s Premier League points total, but
in our sample of 90+ teams who won 10 or more, 80% of them accrued more than
41% of their points from such victories.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Everton won
76% of their 59 points in 2002/03 from single goal wins and then tried their
very best to get relegated in 2003/04 as their “luck” in narrow games returned
to earth and they won just 39 points.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">In
Liverpool’s case in 2018/19, one goal margin wins only accounted for 31% of
their 97 points. Therefore, their ten such wins places them in a group of sides
who typically regress, but the percentage of total points they win in this
manner is entirely atypical of that group.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">To see
where Liverpool stand as being adept at winning single goal margin games, we
need to look at their underlying goals record.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">In 2018/19
they scored 89 and conceded 22, taking the Poisson route, that’s consistent
with winning nine games by a single goal over 38 games. They won, as we’ve seen
ten, hardly a worryingly large over-performance.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">You can
lump Liverpool in with a group of teams who have achieved good things, partly
as a result of “knowing how to win” (Leicester 2015/16 spring to mind, 14
single goal wins where nine would have been a more equitable return), but
unlike most of these sides, the Reds have the underlying numbers to deserve
their record.</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"></span><span style="font-size: large;"></span><br />
<div style="margin: 0px 0px 10.66px;">
<span lang="EN-US" style="margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: large;">Expect a
few more 2-1’s between now and May.</span></span></div>
<b></b><i></i><u></u><sub></sub><sup></sup><strike></strike><span style="font-family: "calibri";"></span>Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-59118110310843219762019-10-21T10:21:00.001+01:002019-10-21T10:35:49.376+01:00Closing the Door.One of the most fun aspects of football data analysis is when the team you're part of derives some exciting newly derived metrics from the raw data that allows you to look at old problems with a new light.<br />
<br />
Some real heavy data lifting has been put into deriving our Non Shot expected goals model. So first a quick recap on what it does.<br />
<br />
Whenever the ball is moved around the pitch there is a likelihood of scoring from each location it finds itself in. We express this value as non shot xG and the difference between these values when an action is completed is the change in NSxG via that action.<br />
<br />
There's also a "risk/reward" aspect for when you concede possession.<br />
<br />
Finally, each team has (nearly always) a different NSxG for the same pitch location, because one major input is the distance to your opponents goal.<br />
<br />
We've mainly looked at passing and ball carrying, so far, quantifying the differing importance to your side of moving the ball five yards out of your own penalty area or five yards into your opponents. But there's an obvious extension of this that flips the focus and examines how well a team prevents an opponent progression the ball.<br />
<br />
This isn't just by making passing difficult, it's also by making it harder or easier for opponents to carry the ball forward as well.<br />
<br />
It used to be call closing a player down, it's called any manner of terms nowadays.<br />
<br />
Here's how sides are fairing in preventing ball progression in 2019/20.<br />
<br />
The first thing you need is a benchmark figure to measure how well a side is closing down the opposition.<br />
<br />
There's only been nine matches played by each Premier League team to date and they may have played a bunch of sides who aren't that good or willing to play out from the back, so we need to find a set of figures that reflect this possible imbalance of intent and talent.<br />
<br />
Let's take Manchester United. They've played nine teams, Chelsea, CP, Leicester, Newcastle, Southampton, WHU, Arsenal, Wolves & Liverpool.<br />
<br />
Those teams, in turn have also played nine teams (except Arsenal, who play tonight), that's 80 teams of which nine are Manchester United.<br />
<br />
That's almost guaranteed to include every Premier League team at least once and makes up a decent sample of around 70-80 games depending upon how you slice it.<br />
<br />
We therefore, we took those 71 non Manchester United matches played by Manchester United's opponents and looked at the "risk/reward" ball progression via <i>both</i> passes and ball carries for 100 pitch segments.<br />
<br />
For each segment we calculated the average NS xG gained (or lost) per 100 pass & carry attempts. That was our baseline for United's opponents progression against a broad selection of opponents this season.<br />
<br />
Then we repeated the exercise, but for these sides in their matches against Manchester United and ran a heat map to see where on the field these teams were finding it difficult to progress the ball against United and where they were having a easier time compared to their benchmark numbers against the rest of their opponents.<br />
<br />
This is what it looks like ( ignore the numbers for now).<br />
<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjqHeda8XP7BH-ewrTHLm0y0W5fkulTO6jAY4vtXatiqS47kL933roizMr8VG9ArrkO39sDizXE990mOcyaEC4ED-N3aQ0mIHDCVcbDedIuzhJT4MyRIRKNScbgS0YTRGPIzsNhGMQhpxQU/s1600/prog+vs+MU.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="556" data-original-width="573" height="620" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjqHeda8XP7BH-ewrTHLm0y0W5fkulTO6jAY4vtXatiqS47kL933roizMr8VG9ArrkO39sDizXE990mOcyaEC4ED-N3aQ0mIHDCVcbDedIuzhJT4MyRIRKNScbgS0YTRGPIzsNhGMQhpxQU/s640/prog+vs+MU.png" width="640" /></a></div>
<br />
The red areas are where United's opponents are progressing the ball at lower levels against United than they've managed as a group against a basket of 71 other Premier League sides. Blue, they're doing better.<br />
<br />
It's a pretty stark and clear picture of where on the field United have been making it difficult for their opponents to get the ball into more dangerous areas. Firstly, beginning in front of their opponent's own box and then aggressively in front of United's own. They aren't too fussed about targeting wide positions on halfway and not too good(?) at stopping runs or passes from the bye-line & in the box.<br />
<br />
Here's Everton and they do harry the opposition, but it's a much more chaotic process, with very little structure, especially compared to United's disciplined approach.<br />
<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEizkXe5NivxDcm9JWCPdRF9dyG89yOWBT8jDTcz-ljBFB8Y6QjHLDcmTFsUPqRTtTfS-3SH7ZsHLPPjRjZ22WBKnwH90Wojt91RU9zAQ1a2uXw8RW8MyYXKfKZXjv55tirfj1DDeY1J0tYx/s1600/eve.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="546" data-original-width="569" height="614" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEizkXe5NivxDcm9JWCPdRF9dyG89yOWBT8jDTcz-ljBFB8Y6QjHLDcmTFsUPqRTtTfS-3SH7ZsHLPPjRjZ22WBKnwH90Wojt91RU9zAQ1a2uXw8RW8MyYXKfKZXjv55tirfj1DDeY1J0tYx/s640/eve.png" width="640" /></a></div>
<br />
And finally, here's Aston Villa.<br />
<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxHzNcq9zQFcw8L7PZy5PAFrJUAdMTacLIH0rISbe3QjhDXJZCeMi22MxMTBSN1ox7famUub_wHE-PiB5po2Ym3N58CWmuVOXhFUfVKGineki1KaINX-E45trk49SXbf82Gge58IIZZzz9/s1600/av.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="546" data-original-width="571" height="610" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxHzNcq9zQFcw8L7PZy5PAFrJUAdMTacLIH0rISbe3QjhDXJZCeMi22MxMTBSN1ox7famUub_wHE-PiB5po2Ym3N58CWmuVOXhFUfVKGineki1KaINX-E45trk49SXbf82Gge58IIZZzz9/s640/av.png" width="640" /></a></div>
<br />
There's no overt closing down of the opposition until they reach the box, at which point it seems to become all hands to the pump.<br />
<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-32374755598132887682019-10-02T10:12:00.000+01:002019-10-02T10:12:24.459+01:00Passing Risk Reward in the Premier LeagueThe availability of richer data sources has naturally led to an interest in passing and ball progression.<br />
<br />
The generally quoted passing metrics still gravitate towards event data such as goal attempts and actual scores as the major framework. <br />
<br />
Passes that lead to a potential goal scoring attempt predominate in most current passing metrics and little has been done to differentiate between the contribution made by individual players involved in these possession chains.<br />
<br />
In contrast, we've broken down the value of each pass attempted by referencing how likely a possession anywhere on the pitch has historically led to a goal, whether or not the possession ultimately result in an attempt on goal.<br />
<br />
This so called non shot xG metric not only allows a route to value every ball progression, be it a pass or a carry, but also quantifies individual involvement, rather than sharing the credit equally between all those participating in the possession.<br />
<br />
However, as often is the case in football metrics, only one side of the ball has been investigated.<br />
<br />
Each pass attempt comes with a risk and reward.<br />
<br />
The player attempting the pass has custody of a valuable team resource, namely the non shot xG value for possession of the ball at that precise position on the field.<br />
<br />
The potential reward in making a progressive pass is to advance the ball to a more dangerous area of the field.<br />
<br />
And the ever present risk is the cost of a turnover. The passing team lose the NS xG value they had by owning the ball and the opponents gain their own NS xG by taking possession of the ball.<br />
<br />
Weighing a player's NS xG leger is problematical, but one way to express the risk reward balance of a players passing performance is to add up the NS xG value of every progressive pass they complete and compare this to the sum of the NS xG he loses through incomplete passes, along with the NS xG gained by the opponent taking possession of his errant attempts.<br />
<br />
For example, in the nascent Premier League, Matteo Guendouzi's completed open play progressive passes have been received at areas on the field that totals 6.69 NS xG.<br />
<br />
On the minus side, his picked off pass attempts has "lost" Arsenal 1.67 N xG. This is made up of loss of pitch position for Arsenal and the combined NS xG value for the opponent based on where possession is won.<br />
<br />
Overall, and without regard for pass volume or minutes played, Guendouzi has a net positive 5.02 NS xG for Arsenal in 2019/10.<br />
<br />
This puts him top of the Arsenal "risk/reward" passing charts and we feel is a much better single figure metric to describe a player's involvement in progressing his side towards the opponents goal.<br />
<br />
Not only does it quantify individual involvement and utilses every pass attempted, it also penalises reckless or sloppy execution that leads to change of possession.<br />
<br />
Here's the current pass risk/reward numbers for all 20 Premier League players with a minimum number of attempts.<br />
<br />
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<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-91248603196832004662019-09-14T11:20:00.002+01:002019-09-14T11:20:42.374+01:00Game State and Blocked Shots.I've written a fair bit about game state and how it impacts on how a side approaches a match s the time elapses and occasionally the score line changes.<br />
<br />
I don't use score differential to define "game state", instead I use a measure of how well each team is fairing based of their pre game expectation. <br />
<br />
This can be defined as the expected points based on the current score and time elapsed or the expected success rate of a team, again when measured against a pre kick off baseline. The choice is entirely up to you.<br />
<br />
The advantage of this approach is primarily when the game is tied (which it is for a fairly significant portion of most matches). Instead of counting offensive production for<i> both</i> sides at this score differential, there's usually a clear indication of which of the two teams is happier with the stalemate and which is not.<br />
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You also get a gradual movement of game state that incorporates the often omitted variable of time elapsed.<br />
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It's intuitive as to what might happen as game state ebbs and flows over the course of a match, as unhappy teams perhaps become more risk taking in order to change the current status quo, while pregame underdogs are forced or chose to attempt to bank their above expectation gains by becoming more defensive.<br />
<br />
One slight problem with this approach is that it assumes a relatively balanced competitive edge between competing teams and further assumes that those needing to change the current scoreline are capable of attempting to do so.<br />
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Not to be harsh, but it's difficult to envisage a situation where Manchester City felt the need to protect a lead against say Newcastle or where Newcastle were technically able to up their attacking intent against the champions.<br />
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So often the presence of clearly superior teams can skew conclusions. "Possession leads to wins" arose largely because better sides also had high levels of possession, but the possession was a byproduct of other things they did, rather than the primary driver of their results.<br />
<br />
Remove Barca etc from the data and the relationship between possession and wins tended to disappear.<br />
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Therefore, firstly here's why "zero goal differential" (the game is level) shouldn't be regarded as a single game state.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhERVSXoDGTyTvb9ku4jcXKGNpV4kZqoyhEzOz1rdRhrE-5DYlMuWlPffNF4szys8c5T82ADFm1NCGz36zq25EJzofMd1WbJV_gcTuJJ9ok7P6Yh3iAsj056oUjIyG1hERVEJ49WbSGuWsf/s1600/shorGS.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="598" data-original-width="1042" height="366" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhERVSXoDGTyTvb9ku4jcXKGNpV4kZqoyhEzOz1rdRhrE-5DYlMuWlPffNF4szys8c5T82ADFm1NCGz36zq25EJzofMd1WbJV_gcTuJJ9ok7P6Yh3iAsj056oUjIyG1hERVEJ49WbSGuWsf/s640/shorGS.png" width="640" /></a></div>
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Here's a sample of matches from the 2018/19 Premier League, involving games where one of the Big 6 wasn't playing. Thus the games weren't particularly one-sided from the outset.<br />
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Initially, I've simply counted the shot volume from regular play for teams when the score differential is zero (the game is level). The vertical axis records my version of changing game state, a larger negative value indicates that a team that is doing badly compared to the expectation at kickoff.<br />
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Typically, this may be when a home favourite is level a fair way into the game and a points expectation that may have been 1.75 expected points at 3 o'clock has fallen back towards one point as the clock ticks on towards 5.<br />
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Those above the blue score differential line of zero are doing better that they hoped for, they might have expected to average less than a point from such a game, but they are edging closer and closer to a point, with a possibility of nicking all three.<br />
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Each point represents a goal attempt and it's clear that the lions share are being taking by the disgruntled favs.<br />
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If we re-examine our intuition, it's likely that if the beneficiaries of the stalemate aren't taking that many shots in the match, they're doing things to prevent the ones at the other end going in.<br />
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Learning from the likes of Pulis and Dyche that will likely include blocking shots.<br />
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Next I built a simple xG model (just location & type), but also included the game state factor, not just at zero goal differential, but at all score differentials to see if it told anything about the likelihood a shot would be blocked or not.<br />
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I eliminated games where a red card had been shown, for obvious reasons.<br />
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The bottom line was that game state was a significant factor in correlating with whether an attempt was blocked or not, along with location and shot type. And the larger the decrease in a side's pre-match expectation when the attempt was taken, the more likely it became that the shot was blocked.<br />
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In short, without the superstar teams, run of the mill games appear to follow the "hold what we have" and "this is disappointing, let's crack on" mentality.<br />
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This is one route to improve the much criticised problem of single xG races, where one team scores early and then drops anchor, but whether it is a universal improvement to a predictive model is a question of over fitting the past and potentially screwing up the future.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-6455548623896607912019-09-11T10:57:00.000+01:002019-09-11T10:57:03.358+01:00Rugby World Cup SimulationWorld Cup's have been like London buses this year and the rugby union version kicks off in a week or so.<br />
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It's live and complete on terrestrial TV in the UK, with plenty of huge mismatches in the opening group games, before eight teams, (whom could be fairly accurately predicted beforehand) hold the really interesting knockout run to the Webb Ellis Trophy on November 2nd.<br />
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However, that's not to say that the group matches don't hold any intrigue. There are at least two tier one teams in each of the four groups and while they'll be expected to steamroller the lower grade group opponents, the outcomes of these elite matchup will have a huge bearing on how the pairings for the knockout phase pans out.<br />
<br />
Therefore, if you want to chart the likelihood of a team's route to the final being paved with Southern hemisphere behemoths, a tournament simulation is the easiest method out there.<br />
<br />
You'll need a ratings system to kickoff with, assuming you're shunning the merry-go-round that has been the world rankings. Ireland are the current leaders, having recently displaced Wales, who had just displaced New Zealand, who themselves had displaced South Africa....ten years ago.<br />
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So the world rankings, following a decade of stagnation have suddenly become volatile.<br />
<br />
Let's make our own, instead.<br />
<br />
I took the last 20 matches for all participants, and produced an attacking and defensive rating, based around match scores and opponent quality.<br />
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New Zealand are the tournament's most potent attack, they'll score around 14 more points against and average team than another average team would manage and Wales, courtesy of rugby league knowhow, has the best defence.<br />
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Next you need a way to simulate game outcomes.<br />
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The big clash of the group stages sees favourites New Zealand take on South Africa. After matching up the respective attacking and defensive ratings for each team, the model expects the All Blacks to average around 28.5 points and S Africa 23.5.<br />
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New Zealand are favoured by five points and there's likely to be 52 total points.<br />
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If we look at the spread of points scored and allowed by each side over the last year or so, we can produce a distribution of points that describes each team's likely scoring pattern in this game. We'll then draw a value randomly from this distribution for each team to simulate a single match scoreline and then repeat the process thousands of times.<br />
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After adding a few tweaks to mimic the largely redundant bonus points system rugby insists on employing and ensuring that each drawn score from the distributions is a "rugby score" (no scoring a grand total of four points etc), we just repeat for every group game, add up the total points won in the group, follow the draw format and find the winner.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEggI0pwyNBoAnpanAjIEdo-O-zwKiCqvKtqIEsgVlvSrkL8oZDKjIsk09yDh-Rdc5xOZSBNhT7ZN1gO0ogxrN1fifoxOwL6UJrOwXDEAnHgnwjs3WPokzwW-Fy0h2fwNhU9UqJqhI4gKz7s/s1600/rwc.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="771" data-original-width="848" height="580" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEggI0pwyNBoAnpanAjIEdo-O-zwKiCqvKtqIEsgVlvSrkL8oZDKjIsk09yDh-Rdc5xOZSBNhT7ZN1gO0ogxrN1fifoxOwL6UJrOwXDEAnHgnwjs3WPokzwW-Fy0h2fwNhU9UqJqhI4gKz7s/s640/rwc.png" width="640" /></a></div>
<br />
This is how the simulations shake out.<br />
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Four sides with a double figure percentage chance of lifting the trophy, New Zealand, S Africa for the south and England and Wales for the north, with the former looking a vulnerable favourite.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-1532869300037839802019-07-02T09:20:00.002+01:002019-07-02T09:20:57.554+01:00Quantifying the Value of Every PassI've written about passing models over the last couple of years and posted passing maps for individual players and teams recently. So here's a quick overview the passing model upon which those maps are based, how it was developed and how they might be useful.<br />
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The model is derived from location and time stamped Opta data for every pass attempt. The model has been build in conjunction with Infogol, but as yet it isn't part of the data available on the Infogol app.<br />
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I was keen to use familiar units for the passing model, therefore all values for successful or unsuccessful passes are expressed in expected goals. <br />
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I've purposely avoided such things as distance gained, as this often leads to arbitrary definitions for "key passes". <br />
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It also breaks down entirely when you approach the penalty area, not only in terms of scaling, but also assigning value to a backward pass that actually adds value to a side if it is completed. (Think pull backs from the goal line, a "progressive" pass can easily go backwards).<br />
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The baseline values are the likelihood that possession at any position on the field will end with a goal and is taken from historical data.<br />
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Therefore, if passing from one point to another improves the likelihood of a goal, the successful pass is quantified as the change in this likelihood.<br />
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Because the unit of measurement is how likely historically, possession is to turn into a goal, it doesn't require a goal attempt to ultimately be made at the culmination of the move.<br />
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This is a huge advantage over passing models that are based solely around attempts being taken because <i>every</i> pass attempt is counted (a player is not reliant on success or failure further down the passing chain).<br />
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It also makes the calculation of speed of attack much more relevant to the actual threat present (advancing the ball ten yards in a couple of seconds from the final third causes much more of a threat than advancing the ball 20 yards in half the time from your own penalty area).<br />
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Finally, because the units relate to aggregated historical outcomes of possessions, we can quickly give a value to any point of the field, which is not the case if the point value is based on the expected goals of an actual goal attempt from that position.<br />
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And so because a goal attempt isn't required the units are designated as non shot expected goals to differentiate them from shot based xG.<br />
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To keep things simple, the following non shot xG passing maps omit other actions, such as carries or dribbles, take no account for time of possession or any likely passing skill differential.<br />
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The following maps simply record any successful, "progressive" pass (by which I mean any pass that advanced the likelihood of a team scoring) made by a player during the last Premier League season.<br />
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The maps are simply conditional formatting in excel, on a 10X10 grid, overlaid with a pitch. 10X10 is used for the convenience of Opta's x,y pitch locations which run from 0-100, lengthwise and widthwise.<br />
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The darker the conditional formatting the more NS xG has been gained from a successful pass from that location. Either by small gains, but large passing volume, large gains and fewer passing volume or a combination of the two. <br />
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It's easy to show the passing distribution through other plots.<br />
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Here's England's newly capped Declan Rice's successful progressive NS xG gains for WHU in 2018/19.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpEXHwE66AVlebWdROFx9E_onMYeVTjiprEfIAE10ZgMUdwyOnJ6EzHJ-Ddv9a_2Fj2m3wth4EAk_Hm1w_0eotsM73phhXej9yeoQyFgc2ZLiqlumzJWnCbu-omrqywn2EqtTnZEtrr9mO/s1600/decl+start.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="457" data-original-width="626" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgpEXHwE66AVlebWdROFx9E_onMYeVTjiprEfIAE10ZgMUdwyOnJ6EzHJ-Ddv9a_2Fj2m3wth4EAk_Hm1w_0eotsM73phhXej9yeoQyFgc2ZLiqlumzJWnCbu-omrqywn2EqtTnZEtrr9mO/s1600/decl+start.png" /></a></div>
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This represents the starting point of every successful pass.<br />
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The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield.<br />
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The next thing we'd like to know is where these passes end up, so the following plot illustrates where on the field this improvement in NS xG production from Rice via his passes is distributed and received by a team mate.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgypmWX_CkQrZQij38Ya_SbL1A5GNtFGy8DVONibESlKufLeE5fmj4Em3IYkPnFu1De8oWaaoU14qjSKXUFPc6HdapX6fW8CC0ecKElLzbfIMW8aWzdz6Kpjl_D42JvFal43rrFFOYn0YZN/s1600/declend.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="457" data-original-width="626" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgypmWX_CkQrZQij38Ya_SbL1A5GNtFGy8DVONibESlKufLeE5fmj4Em3IYkPnFu1De8oWaaoU14qjSKXUFPc6HdapX6fW8CC0ecKElLzbfIMW8aWzdz6Kpjl_D42JvFal43rrFFOYn0YZN/s1600/declend.png" /></a></div>
Overall Rice's progressive passes are received around 10% further upfield than their points of origin. He spreads the ball wide, as noted by the darker areas on the flanks either side of halfway and towards the final third. And he finds a team mate on the edge of the box, but doesn't appear to be a predominate passer of the ball into the box (particularly if we strip away set plays).<br />
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Rice appears to be an active and productive passer over around three quarters of the playing area, but may not be fully appreciated because he rarely plays a pass that may be considered an assist.<br />
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By contrast, here's a much more attacking NS xG passing profile from Manchester City's midfielder, David Silva. A darling of the highlights reel.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_Vvki9XMbtY06Rkoy4YnGU2Onq0aIL9lw_fnfJgK_HqdTs-YNGA2Eyt-4Wdd32MX30M8m-S9j4skqU9hVwosg9L4P-ojkBLE4YkLJIIyqZbnMA2VOZlWM3sGQYibJw0I7IDXNAdkKr4UI/s1600/sil.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="457" data-original-width="626" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_Vvki9XMbtY06Rkoy4YnGU2Onq0aIL9lw_fnfJgK_HqdTs-YNGA2Eyt-4Wdd32MX30M8m-S9j4skqU9hVwosg9L4P-ojkBLE4YkLJIIyqZbnMA2VOZlWM3sGQYibJw0I7IDXNAdkKr4UI/s1600/sil.png" /></a></div>
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Unlike Rice, Silva rarely ventures into his own half to begin build up play. The starting point for his progressive passing is a hot spot just outside the left edge of the opposition penalty area, although he does occasionally drift to the opposite side of the box. <br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgFfxJCAxSSLlGJjRDaShsuIb9rsVQOVEAEQeKkTEQ-bQH9aCWhy9QMCWU0zUgTMuBO_SEZ3P-3lr1IC-HOaL91iyCybqsWzYZXcd4z39uIJRwafx3kZP3l63oWifxrn14rbA7cq_H7j23A/s1600/silaend.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="457" data-original-width="626" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgFfxJCAxSSLlGJjRDaShsuIb9rsVQOVEAEQeKkTEQ-bQH9aCWhy9QMCWU0zUgTMuBO_SEZ3P-3lr1IC-HOaL91iyCybqsWzYZXcd4z39uIJRwafx3kZP3l63oWifxrn14rbA7cq_H7j23A/s1600/silaend.png" /></a></div>
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The end point of his passing is again strongly centred around the left side of the field, but deep into the opposition box. He sticks rigorously to the left sided channels and relatively shuns pass attempts to the right side of the box from his team's perspective.<br />
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Finally, for now, we can also show where a player is showing up as the recipient of a progressive pass.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgebB6VmnfI2TBxbVsvrhN_XAxflZMIj3iQ7ptRWo7Lsz5JCeKkPKsVQvxaVzVHCkYNTh5xM8q6r6u6EUSTpMrrXjSbKaJTduoSlMDz2v0PfRGfCRPdgqdECrPdL9w4kVX23FjBC1sgM52K/s1600/silrece.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="457" data-original-width="626" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgebB6VmnfI2TBxbVsvrhN_XAxflZMIj3iQ7ptRWo7Lsz5JCeKkPKsVQvxaVzVHCkYNTh5xM8q6r6u6EUSTpMrrXjSbKaJTduoSlMDz2v0PfRGfCRPdgqdECrPdL9w4kVX23FjBC1sgM52K/s1600/silrece.png" /></a></div>
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Once again his fondness for linking up with a team mate in the dangerous left hand side of the area is shown, firstly by the darker formatted green area just inside the left flank of the area on the plot and secondly in an actual example from a game.<br />
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<br />
This just scratches the surface of how these plots, maps and quantified valuing of passes can be useful in assessing a side, or a player. It is particularly welcome because it removes the highlight reel aspect that blights player assessment (particularly on youtube immediately following a transfer). We can see from the heat maps if creative passing into particular areas of the field is a largely consistent player trait....or if the exceptional pass, that perhaps resulted in a goal was a once in a lifetime fluke.<br />
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This post has concentrated on progressive, NS xG gaining successful passes, but it can also be applied to unsuccessful attempts to measure risk reward, the probability of a pass being completed can also be added and we can also look at ball retention plots to see which players excel at retaining the ball for others to make the decisive progressive deliveries.<br />
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Rice and Silva obviously play different midfield roles in widely differing teams, but their respective importance and discipline in playing a role it those two systems becomes much more apparent once we look at their passing contribution as a whole.<br />
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<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-67522753427537710912019-06-11T19:56:00.000+01:002019-06-11T19:56:57.987+01:00The Best & Worst Passers in the 2018/19 Premier League.This is essentially just a data drop of the passing abilities for every player who made at least 600 pass attempts in the last Premier League season, based on a non shot passing model.<br />
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Here's our approach.<br />
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Every inch of the pitch has a non shot expected goal value associated with it based on the likelihood a side will eventually score from that field position.<br />
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So it's very low if you have possession near your own goal, much higher if you possess the ball inside the opposition box.<br />
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Successfully passing the ball from one point to another leads to a change in NS xG.<br />
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If you have the ball on the edge of your own box and roll a pass five yards forward to a defensive midfielder, you get credited for improving the side's NS xG, but not by much. Repeat the move on the edge of the opponent's box and you'll get a fair bit more.<br />
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Knock the ball backwards and your side "loses" NSxG, but at least you keep the ball.<br />
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Give away possession, either as a defender accidently passing to an opponent near your goal and you lose a combination of the NS xG you had and the NS xG your opponent gains.<br />
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Similarly, try and fail with a tricky pass inside the opponent's final third and you lose the fairly substantial NS xG your side had, along with the much smaller NS xG the opposition has gained.<br />
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This has led to three definitions for types of passes, two successful and one not.<br />
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Firstly, <b>successful, creative</b> passes that improve a team's NS xG.<br />
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Then, <b>successful</b>, backward passes that <b>retain</b> the ball, but "loses" NS xG<br />
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And finally <b>unsuccessful</b> passes that <b>turnover</b> possession.<br />
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These are further normalised for position played. <br />
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A defender will have a very different average profile in each category, compared to an attacking midfielder and the metric is also normalised to 100 passing attempts to put players who play for a possession poor team on a more level footing with Manchester City.<br />
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Here's an example.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhWNk1cF6X_CdrryEx5MPv9Fnkf1oJ2AsEt7qbl-9UpRP17TQckzv-jwf_N0yhx3vPJgT8ZorkYYMaQ085XE0oCXL3-LWQb1YE_ySdyDvBdUsHaSKB7cIq8oPG3ebkf7qreeW5z5nW9NAxu/s1600/8.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="292" data-original-width="867" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhWNk1cF6X_CdrryEx5MPv9Fnkf1oJ2AsEt7qbl-9UpRP17TQckzv-jwf_N0yhx3vPJgT8ZorkYYMaQ085XE0oCXL3-LWQb1YE_ySdyDvBdUsHaSKB7cIq8oPG3ebkf7qreeW5z5nW9NAxu/s640/8.png" width="640" /></a></div>
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From left to right. The average Premier League full back adds 0.64 non shot xG per 100 passing attempts by way of successful, <b>creative</b> passes. TA-A added 1.026 NS xG/100, an improvement of 0.386 on the average full back.<br />
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Backward, successful passes where NS xG was "lost", but possession was <b>retained</b> mirrored the average experience of a full back.<br />
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An average full back actually lost 0.8 NS xG / 100 via <b>turnovers,</b> TA-A did slightly worse, losing 0.894, but this is a function of the risk/reward balance. He is given free rein to get into advance positions, but the reward is well worth the extra risks taken.<br />
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Here's the differentials for every player who made at least 600 passing attempts for all 20 clubs last season.<br />
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They've been normalised for position, but many are a product of the role they are asked to play and the stylistic approach of the team they represent.<br />
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Figures such as these cannot tell the entire story, pass volume in particular will be hugely relevant, but we can take a lot from the tables.<br />
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For instance, there's the different roles of goal keepers. Those who play out from the back, such as Alisson & Ederson added below average creativity, but are well above average when preventing turnovers.<br />
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Similarly, van Dijk is no more than an averagely creative passing centre back, <span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">but again the systematic demands of the team do not require him to be more adventurous. <span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">His main aim is to largely play unadventurous ball to slightly advanced players and again, not turn the ball over, which is reflected in his well above average turnover numbers.</span></span><br />
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Manchester City's adherence to keeping the ball is shown again by the turnover figures, with the perhaps significant exception of Sane, who is poor at retaining the ball, with little above creativity to compensate.<br />
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Passing volume ensures that their relatively unexceptional creativity, De Bruyne aside, invariably overwhelms an opponent.<br />
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And finally, t<span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">he departing Hazard is a rare beast, who not only is above average creatively for his position, but also avoids the often boom or bust cycle by looking after the ball exceptionally well.</span><br />
<span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><br /></span>
<span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">There are plenty of players who show above average creativity, but pay a relatively high price with turnovers.</span><br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-26402757451380948572019-05-15T12:12:00.001+01:002019-05-15T12:23:52.555+01:00Non Shot Passing Profile for Liverpool 2018/19Over the season, we've slowly introduced a non shot xG model in this blog.<br />
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We assign the likelihood that a goal will be scored (or conceded) by a team in possession at any location on the field.<br />
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Successfully advancing or turning the ball over at another position on the pitch changes the non shot xG for the possession and the difference between the two points can be used to quantify the on field action.<br />
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This framework can be used however the ball is moved, but an obvious single application is to evaluate passing and the resulting risk reward.<br />
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The approach sidesteps the need for a shot to be attempted to assign a value to an action, differentiates between safe passing with little purpose and includes a huge chunk of data that was previously ignored.<br />
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You can generally differentiate between two types of passing actions, one that advances the ball into a more dangerous position and one that moves the ball backwards to recycle a move.<br />
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These can obviously be further divided into successful and unsuccessful actions.<br />
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Therefore, at its broadest we can identify a player's non shot passing contribution into value added and lost by successful or unsuccessful attempts to progressively move the ball into a more dangerous area.And similarly, NS xG "lost" by a successful backward pass, where possession is maintained and potentially more harmfully, NS xG actually lost when unsuccessfully passing the ball towards one's own goal.<br />
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If we incorporate minutes played and overall team style, we may begin to identify important contributors and ways that a side attempts to move the ball around the field.<br />
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Here's Liverpool's Premier League season from 2018/19.<br />
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I've highlighted NSxG gained & lost from forward passes & that "lost" by successfully recycling the ball away from the opponent's goal.<br />
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The passing performance of the player's broadly splits into 4 separate categories.<br />
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Keita & Henderson take a back seat to the players in groups 2 & 4 when creating dangerous completed passes, but do frequently recycle the ball backwards.<br />
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Henderson has contributed 5% of the NS xG gained by Liverpool from a forward pass & accounted for 8% of the recycled, backward NS xG.<br />
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Group 2 are most active creatively, but do turn the ball over a lot. Although, that inevitably comes with the territory in which they operate and so you assume the two columns are an acceptable trade off.<br />
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Someone has to be entrusted with turning a good situation into a great one, even at the cost of losing the ball to an opponent.<br />
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Group 3 accumulate the lowest amount of improvement in NS xG, presumably by beginning moves from relatively deep areas and VvD aside, being relatively unadventurous.<br />
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The final group 4 are also fairly creative, operating in areas where even a short, completed pass can have a relatively large effect on NS xG and again the trade off is that often a large chunk of NS xG with which they have been entrusted can be quickly lost.<br />
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This group also retains possession, but cedes NS xG through laying the ball back from advanced areas of the field.<br />
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We might assume that these figures are the benchmark requirement for each position or group in the current Klopp side.<br />
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<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-17126781985633529152019-03-06T09:25:00.000+00:002019-03-06T09:45:50.462+00:00Title Winners Aren't Becoming More Dominant Over Time.Are the title winning teams in the Premier League getting more dominant because they're getting so much richer?<br />
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It seems a logical conclusion to draw given that Manchester City won the league with an unprecedented 100 points in 2017/18.<br />
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That obviously makes them the highest points per game team in 20 team Premier League history, but without context, such figures are largely meaningless.<br />
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Taking the points per game high point as a selective cutoff point is invariably going to furnish any number of apparently positive trendlines, but without taking a deeper look at how the league as a whole has evolved over a period of time, they too are context-less trivia.<br />
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The first 20 team Premier League season in 1995/96 had 98 draws, by 2017/18 the number had risen....to 99. But singular seasons may hide an upward or downward trend and this appears to be the case with drawn matches and by extension the total points that were won in a whole season.<br />
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The 1990's averaged 104 draws per season compared to just 92 for the comparable number of most recent Premier League campaigns.<br />
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Here's what this means for the average number of points won by sides in each Premier League season since 1995/96.<br />
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There has been a steady upward trend for the average number of points won by all Premier League teams since the beginning of the 20 team era, as draws have tended to decrease, therefore reducing the number of matches where just two points are won compared to those where three are gained.<br />
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So are the top teams taking a bigger share of this expanded points pot, which may indicate that they are being more dominant that their predecessors were.<br />
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One way to look at this context corrected view is to see how remote the representative of each finishing position has become from the average points won by a side in a particular season.<br />
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Manchester City in 2017/18 were 2.5 standard deviations above the league average points won that season. But it's a level of dominance that was very similar to that attained by Chelsea in 2004/05, Arsenal in 2003/04 and Manchester United in 1999/2000.<br />
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Here's the plot of how far from the average points all 20 finishing positions have been since 1995/96.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX-3PIR1Ql7vveWE8OBEPKLEzRwrDuR6TXdF9JZH8V9ELFy0nuFxqYwFxqwigUKhKTowwRr-HQ49TvhF1EQyKgfQyswJPbW2bmXMqEClexRZZsqznSVwjvM9_R4sbzMUQonAKfxTvN8I10/s1600/dom.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="686" data-original-width="1139" height="384" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX-3PIR1Ql7vveWE8OBEPKLEzRwrDuR6TXdF9JZH8V9ELFy0nuFxqYwFxqwigUKhKTowwRr-HQ49TvhF1EQyKgfQyswJPbW2bmXMqEClexRZZsqznSVwjvM9_R4sbzMUQonAKfxTvN8I10/s640/dom.png" width="640" /></a></div>
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OK, it's messy. But it's fairly easy to see that the title winners aren't powering upwards in a ever improving arc. In fact it pretty much flatline's and might even be encouraged to dip downwards if we wanted to be "creative".<br />
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Here's an easier on the eye trendline for each final position.<br />
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Once you add the context of the points gathering environment over time, Man City 2017/18 are just a bump in the road and not part of a general trend. None of the top three finishing positions have shown to have improved their dominance over the rest of the league.<br />
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There's been a slight uptick for 4th to 7th placed sides, a down tick for 7th to 12th. Then everyone holds station, until the two worst teams become slightly more competitive over time, but still go down.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-60037185453678196122019-02-21T11:08:00.000+00:002019-02-21T11:08:42.191+00:00The Name Game.Sports analytics, not just football (or soccer) has always had a problem when naming their metrics (see what I mean).<br />
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Corsi, TSR, Pythagorean and expected goals may work fine in a closed environment, but try sticking those terms into the mainstream and you're immediately on the back foot.<br />
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Jeff Stelling's rant wouldn't have been half as effective if he'd had to say "Chance quality, what's that!"<br />
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Anyway, we've already embarked on a second phase of attaching names to a brand new raft of models and performance indicators, except this time <i>everyone's</i> going to be scratching their heads about what it is that we're actually talking about.<br />
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Anyone who's ever posted an xG figure will be familiar with the "X get Y for their xG, why the difference" but the rise of the NS xG model will take that to new heights.<br />
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Shot based xG models <span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">(actually shots, headers and other body parts)</span> all share a core set of inputs (location, type) and any additions simply move the dial slightly, but the steady onset of so call "Non Shot xG" models may lead to comparisons between models that bear very little relationship to one another.<br />
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538 has a NS xG model, defined thus,.<br />
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<span style="-webkit-text-stroke-width: 0px; background-color: transparent; border-bottom-color: rgb(34, 34, 34); border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: rgb(34, 34, 34); border-left-style: none; border-left-width: 0px; border-right-color: rgb(34, 34, 34); border-right-style: none; border-right-width: 0px; border-top-color: rgb(34, 34, 34); border-top-style: none; border-top-width: 0px; box-sizing: border-box; color: #222222; font-family: AtlasGrotesk,&quot; font-size-adjust: none; font-size: 17px; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: 700; letter-spacing: normal; line-height: 23.8px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; orphans: 2; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; vertical-align: baseline; white-space: normal; word-spacing: 0px;">Non-shot expected goals</span><span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: #999999; display: inline !important; float: none; font-family: AtlasGrotesk,"Helvetica Neue",Helvetica,Arial,sans-serif; font-size-adjust: none; font-size: 17px; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; line-height: 23.8px; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"> is an estimate of how many goals a team could have scored given their nonshooting actions in and around their opponent’s penalty area.</span><br />
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<span style="-webkit-text-stroke-width: 0px; background-color: transparent; color: black; display: inline !important; float: none; font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">Infogol has a NS xG model, but ours is based on the expected outcome of possession chains.</span><br />
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They currently share a name, but nothing else.</div>
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In an increasingly monetized situation it is understandable that some are reluctant or unable to share detailed descriptions of each model's makeup.</div>
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But, even if we can't avoid falling into the trap of using less than intuitive language to name commonly used metrics (as happened with xG), we perhaps should steer clear of using catch all terms, such as NSxG to describe future modelling efforts.</div>
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538's model appears to be event based, ours is possession based, so it's probably best to include this additional piece of information when presenting any NSxG models in the future. </div>
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-67463628257311639162019-01-31T11:43:00.000+00:002019-01-31T11:58:05.920+00:00A Non Shot Addition to the xG FamilyShot based expected goals models can tell us a lot about a match by extending the sample size from around three for actual goals to well into double figures for goal attempts.<br />
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But they are event based descriptions of a match and don't always tell the whole story of a match.<br />
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The weakness of event based models, be they attempts, final third entries or touches in the box, is, rather obviously, that these event have to occur for them to be registered, often in the most competitively contested region of the field.<br />
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<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">Non shot xG models can fill the void that sometimes exists by examining such things as possession chains and the probabilistic outcome that may occur between two teams of known quality.</span><br />
<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><br /></span>
<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">Last night Liverpool drew 1-1 at home to Leicester.</span><br />
<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><br /></span>
<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">The hosts, depending on your view point, were unlucky to lose because, "Leicester defended well", "Atko reffed the game poorly" or "Liverpool weren't themselves".</span><br />
<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><br /></span>
<span style="background-color: transparent; color: black; display: inline; float: none; font-family: "times new roman"; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">Shot based xG universally gave the match to Leicester. They created better chances and had a larger total shot based xG than the title contending Reds.</span><br />
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Here's Infogol's shot map from last night. Leicester created a couple of decent chances. Liverpool were restricted to attempts from distance.<br />
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However, if we look at the potential return for each team based on where and how frequently they began attacks against each other, combined with the typical outcome of such possession in expected goals terms and the talent based differential at completing or supressing passes or dribbles, the balance of "probabilistic" power shifts.<br />
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Liverpool shaded the non shot xG assessment by 2.4 to 1.1.<br />
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They had the ball frequently enough, beginning in sufficiently advanced areas to have scored a likely two or three goals, with a penalty thrown in for good measure. <br />
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Leicester would have typically replied once.<br />
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So why was it just 1-1.<br />
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Just plain randomness ? An early goal that caused Liverpool to cruise somewhat in a similar way to the return game earlier in the season. A clever Leicester game plan that frustrated Liverpool with a packed defense and a bit of luck from the officials.<br />
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There's no correct answer, but there are tools, both event and possession based that can add clarity and suggest areas of investigation.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-58999387781198865082019-01-29T10:01:00.002+00:002019-01-29T10:01:36.617+00:00Simulating Post Game Outcomes with a Non Shot xG Model.First there was xG, ExpG, expected goals, chance quality or whatever you wished to call it.<br />
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Then we simulated the shooting contest to create a likelihood and range of <a href="http://thepowerofgoals.blogspot.com/2013/08/stoke-2-crystal-palace-1-shot-analysis.html"><span style="color: blue;">possible scores.</span></a><br />
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Next we added the different scoreline probabilities to arrive at a post game chance of the shooting contest ending as a win or a draw.<br />
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Undeniably these approaches help to illuminate the story of a single game, but there are occasions when a shot based approach can mislead.<br />
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Game state, (the combination of time remaining, scoreline and the talent differential of the two teams), can sometimes lead to a side prioritising winning the game as opposed to maximising the number of goals they may score.<br />
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The obvious example of these game state effects might be a side leading by a single goal deep into stoppage time heading for the corner flag, rather than the opposition penalty area or the reverse where a trailing team attempts a speculative long range effort instead of choosing to progress the ball and perhaps losing it before they can shoot.<br />
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Therefore, a simple xG tally can sometimes become distorted by attempts that aren't taken and attempts that perhaps shouldn't have been.<br />
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Non shot xG models may provide a partial solution to this occasional disconnect between xG totals and an eye witness account of a game.<br />
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Instead of using goal attempts when assessing the performance of each team, possessions my the chosen currency in a non shot chance quality model.<br />
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Non shot xG models aren't too concerned with how a team choses to use their possession. <br />
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Instead it takes a weighted midline between the situations where scoring a goal is the main aim and when alternatively preserving a lead is paramount.<br />
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A side who isn't being overwhelmed by a trailing opponent can therefore still build up non shot xG credit by claiming a fair share of possessions in varying areas of the pitch......even if they don't chose to convert them into actual goal attempts that would register in a shot based xG framework.<br />
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In short, a side may go shot-less for the final half hour in a game they lead, but still be largely in control of managing the advantageous scoreline.<br />
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Earlier this season, Liverpool went to Huddersfield and won 1-0 with a Salah goal in the 24th minute.<br />
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Huddersfield "won" the shot based xG contest 0.9 to 0.6 and whether you want to simulate every chance (some of Liverpool's were related opportunities) or simply run the relative xG totals through a poisson, you'll find that shot based xG thinks that Huddersfield were more likely to win the actual game than Liverpool.<br />
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The 1X2 splits are around 40/35/25.<br />
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So this is one of those occasions when shot based xG thinks the wrong team won, although it is blind to the superior team holding an early lead.<br />
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However, a possession based, non shot model, which values every possession and doesn't need a goal attempt to trigger a plus for either teams sees things rather differently.<br />
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Liverpool's possessions were, on average around 15% more valuable than Huddersfield's.<br />
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I only vaguely remember watching the match, but I didn't get the impression that Liverpool were very lucky to win, nor that, if needed they wouldn't have turned their superior possession chains into more chances.<br />
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If we now simulate the likelihood of each side turning their possessions into goals (with no regard for tactical, game state related nuances), Liverpool now win a non shot simulation 44% of the time compared to just 26% for Huddersfield.<br />
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There is no right answer when looking at who deserved a win or a loss, and while shot based xG offers one probabilistic opinion, as they say others are available and sometimes they will disagree.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-22598946606982410092019-01-25T16:43:00.000+00:002019-01-25T16:43:19.772+00:00Putting Together a Possession Based Non Shot ModelI've previously written about non shot based models as an alternative to purely shot based xG, as well as a way of incorporating the 90+% of onfield actions that are omitted in the former.<br />
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A valid criticism of shot based models is that a goal attempt needs to be registered before expected goals tallies can be increased.<br />
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However, it is intuitively realised that continued incursions deep into an opponent's half are dangerous, even if a shot isn't forth coming and a dangerous ball that is played across the face of goal also carries a non recorded level of threat.<br />
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Similarly, a penalty kick gives a disproportionately large xG figure, particularly when compared to numerous other passes into the box that don't result in a reckless lunge and a favourable ref.<br />
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An alternative approach might be to count attack based events, such as final third passes or progressive runs and relate these to a likelihood of scoring. But this seems rather arbitrary and lacking a framework.<br />
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Our approach is to select a consistent unit to describe the model that is analogous to a goal attempt and we've chosen a possession.<br />
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We then need an equivalent figure to the expected goal figure for an attempt made on goal. And just as a shot based xG model is driven by the probability of scoring with a shot/header given a variety of identifiable parameters, we have used the likelihood that a possession will result in a goal.<br />
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Shot or header location are the primary factors n a shot based xG model, but modellers have shied away from such things as finishing skill and or goal keeping prowess, as the proliferation of statistical noise often swamps any signal.<br />
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However, in the more event rich environment of passes and ball progressions we may be more confident in including such skill differentials into a non shot model, without straying too far into xG2 shot based territory.<br />
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Anyone who watched Burton's second leg game with Manchester City couldn't not be swayed by the obvious individual and technical ability on show from City compared to their hosts. And the implied level of goal threat was much higher when City gained possession compared to the Brewers in a similar pitch location.<br />
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Therefore, in constructing a non shot based model, as well as such familiar universals as location, we also incorporate factors which identify both above average proficiency in passing as well as in disrupting passes or carries.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOxw3WMPOxS4frM3lrhLiyRCeIF0l3n7w0lyTReXYNVGhglHsBN1Zeofv9oQR1egLKbP2cawZxk7WjRYljHlXWz7eIOjaPPiLnFcoWafXI5lUPQVz7NY16DH3KgMG6TL7fR8l3tnfNBr-G/s1600/pass.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="539" data-original-width="589" height="584" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOxw3WMPOxS4frM3lrhLiyRCeIF0l3n7w0lyTReXYNVGhglHsBN1Zeofv9oQR1egLKbP2cawZxk7WjRYljHlXWz7eIOjaPPiLnFcoWafXI5lUPQVz7NY16DH3KgMG6TL7fR8l3tnfNBr-G/s640/pass.png" width="640" /></a></div>
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Here's a table I posted at the end of last season, showing the level of over or under performance for Premier League teams in pass completion and pass disruption.<br />
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It's notable that Man City were the best at completing passing sequences and suppressing opponent's attempts.<br />
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We now have an assembly of ingredients to produce a non shot equivalent to the purely shot based model.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJvt2nlcWWxzxgiOz8VwBtnXdjcU36RFitBQa4KafVdiuy6XY5qjkDMSsYZKNclik9nIvcnxvGP8A1tFzfyRBxW0WffBiTE4l0F-fMxzjQINOhUkFd2QO_EwxOdfIHu0ZZloQzTXQTqVlx/s1600/passmc.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="653" data-original-width="1194" height="348" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJvt2nlcWWxzxgiOz8VwBtnXdjcU36RFitBQa4KafVdiuy6XY5qjkDMSsYZKNclik9nIvcnxvGP8A1tFzfyRBxW0WffBiTE4l0F-fMxzjQINOhUkFd2QO_EwxOdfIHu0ZZloQzTXQTqVlx/s640/passmc.png" width="640" /></a></div>
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Above is a game by game summary of the non shot xG differential for Manchester City in 2017/18.<br />
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Unsurprisingly, a team committed to possession and passing excellence, with high quality players almost always creates a possession environment that gives them a superior non shot xG differential.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6BcH0aETQJ6_VKL0utPkbtvyBzgfjcmp5VSoOIuD7PdheH0cixNi7H-lyr8BsEa7aWawR92Pb15j5wKxhY9L3uB-Z7VkRpFuSu4KlvZsyIC7GW8eyLfNWigwuaaYLUxZUpUQK0PVLk_gp/s1600/passliv.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="841" data-original-width="638" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6BcH0aETQJ6_VKL0utPkbtvyBzgfjcmp5VSoOIuD7PdheH0cixNi7H-lyr8BsEa7aWawR92Pb15j5wKxhY9L3uB-Z7VkRpFuSu4KlvZsyIC7GW8eyLfNWigwuaaYLUxZUpUQK0PVLk_gp/s640/passliv.png" width="484" /></a></div>
And here's a game by game tally for Liverpool in 2017/18<br />
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Together with a shot based approach, a non shot model can perhaps add nuance to the balance of power between two sides, based on the frequency, location of possessions and pre game skill differentials of the sides, as well as exploring, via a shot based xG model the, now familiar occasions where a goal attempt was generated.Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-7404836549147367942019-01-17T10:10:00.000+00:002019-01-17T10:23:50.832+00:00A Non Shot Expected Goals Look at the UCL Group Stages.The last post looked at quantifying the increased contribution made by players attempting progressive passes based on the improvement in non shot expected goals via completing a pass and the likelihood that an average passer is able to successfully make such a pass.<br />
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We've been building non shot xG models for a few years, so lets take a look at how possession & passing ability can be redefined in terms of non shot xG from this season's UCL group games.<br />
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Once you have a NS xG framework you can look at the risk/reward of every attempted pass by quantifying the improvement in NS xG should the pass be completed.<br />
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This can be further combined with the likelihood a pass is completed against the risk of losing the initial NS xG you owned and handing NS xG to the opposition should they take possession.<br />
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To simplify the post, I'll just look at the reward side of the bargain and aggregate the expected value of a completion in NSxG units for all progressive passes attempted by the 32 UCL group teams and compare that value to the actual value of the completions they made.<br />
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This will quantify how often a side had possession in a dangerous area of the field and if, through better passers and/or receivers they outperformed an average passing team.<br />
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We'll also take a look at the value of passes allowed into dangerous areas and whether a side managed to reduce that value by making it difficult for opponents to complete passes compared to an average defence.<br />
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The defensive side of the ball is often ignored or described entirely in terms of completed actions, such as tackles or interceptions, with little context.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOOHSAcavfo7HExmcXX0mcTiTDxvJd6yRsR4qvgLrs1FGiShHQCZVUDsWWTOvfVenuAeXSYhCuLSr9i_XDsEaViGhxoeBPA1d6GyTvIFkeKteU-SlQCJ4OBEFkFscaFJtYgeBiQfJ1RSo1/s1600/NS.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="839" data-original-width="813" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOOHSAcavfo7HExmcXX0mcTiTDxvJd6yRsR4qvgLrs1FGiShHQCZVUDsWWTOvfVenuAeXSYhCuLSr9i_XDsEaViGhxoeBPA1d6GyTvIFkeKteU-SlQCJ4OBEFkFscaFJtYgeBiQfJ1RSo1/s640/NS.png" width="620" /></a></div>
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The "Attacking Reward from Progressive Passes NSxG" column is the model's average expectation that a progressive pass results in a possession somewhere on the field.<br />
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Playing a forward pass out of defence to the centre circle is very likely to be completed, but the value of the possession in the centre circle won't be that large.<br />
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Playing the ball into the opponent's penalty area, dependent upon the origin of the pass, won't be as easy to complete, but will result in a relatively large NS xG value if it is.<br />
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Overall, if an average team was willing and able to attempt the pass attempts of Real Madrid in the group phase, they would expect to accrue a cumulative NSxG of 74.2 NSxG over the six games.<br />
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Real actual gained 77.9 NSxG.<br />
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So they made lots of dangerous pass attempts (although they did also recycle the ball backwards) and over performed the average model by 4% based on actual completions.<br />
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Porto was one of the better defences. They allowed side's to make progressive passes worth a model value of 39.4 NS xG and restricted the completions to further depress the actual value to 36 NS xG over the six games.<br />
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The best offensive and defensive performers, in terms of NS xG accrued or allowed, along with above average efficiencies are shown in blue, underperformers in red.<br />
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Attack and defensive numbers are correlated, particularly from a possession standpoint. As Swansea showed possession can be a purely defensive strategy. So it makes sense to look at the attacking and defensive differentials, along with the performance of the 32 teams in the group phase.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9ujDZ_5KI3YgPuHTsi1Gvrc6lP2NmsR8f3DMvVIcKB3DA01d7n5-_pgdSVLaHjvbYakmq2yk_Kp4mMCiMGPv6rZ9QigbSMbbGQ5RYO1C8NFKsUeJ0LZjSt9bhLoy7PIJmyUtrRP7Jhtre/s1600/NS1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="839" data-original-width="579" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9ujDZ_5KI3YgPuHTsi1Gvrc6lP2NmsR8f3DMvVIcKB3DA01d7n5-_pgdSVLaHjvbYakmq2yk_Kp4mMCiMGPv6rZ9QigbSMbbGQ5RYO1C8NFKsUeJ0LZjSt9bhLoy7PIJmyUtrRP7Jhtre/s1600/NS1.png" /></a></div>
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Real Madrid had a net positive NSxG differential of +44.2 in topping group G and Crvena Zvezda a whopping -57.8 in propping up group C.<br />
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Real got the ball often into dangerous positions with above average efficiency and restricted the ability of opponents to do the same at league average efficiency.<br />
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This is a step towards quantifying progressive passes, rather than simply counting final third completions etc. It unsurprisingly tallies with actual performance and provides a framework to produce possession chain based evaluations of past and future games that isn't entirely reliant upon a shot based approach.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-46154518875446988662019-01-15T09:20:00.001+00:002019-01-15T09:20:11.983+00:00Quantifying Passing Contribution.Passing completion models have seeped into the public arena over the last couple of months, mimicking the methodology used in expected goals models.<br />
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Historical data is used to estimate the likelihood that a goal is scored by an average finisher based primarily on the shot type and location in the case of expected goals models. And a similar approach is used for passing models.<br />
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Historical completion rates based on the origin and type of pass is combined with the assumed target to model a likelihood that a pass is completed and actual completion rates for players are then compared with the expected completion rate to discern over and under performing passers.<br />
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However, this approach omits a huge amount of context when applied to passes.<br />
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A goal attempt has one preferred outcome, namely a goal. But the unit of success that is often used in passing models is a completion of the pass and that in itself leaves a lot of information off the table.<br />
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How much a completed pass advances a side should also be an integral ingredient of any passing model. Completion alone shouldn't be the preferred unit of success, because it isn't directly comparable to scoring in an expected goals model.<br />
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A player can attempt extremely difficult passes that barely advances the team's non shot expected goals tally. For example, a 40 yard square ball across their own crowded penalty area is difficult to consistently complete and the balance of risk and reward for success or failure is greatly skewed towards recklessness.<br />
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Completing such passes above the league average would mark that player as an above average passer, but if we include the expected outcome of such reckless passes, we would soon highlight the flawed judgement.<br />
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The premier passer of his generation is of course Lionel Messi. It isn't surprising that he would complete more passes than an average player would expect to based on the difficulty of each attempted pass.<br />
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But we can add much more context if we include the risk/reward element of Messi's attempted passes.<br />
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A full blown assessment of every pass Messi attempted in the Champions League group stages becomes slightly messy for this initial post. Instead I'll just look at the positive expected outcomes of his progressive passes.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJzbvyjfFgjD-_vSLUB5aXDiE5uJ9XGDTvgdRSu4gFyQksn0y0_1QwTpQnOLt7YsTDmvxHpLhEYrZ_H_4HhYKSlOSRP9c7j2fc-tlWG-Q3bCiKt0SvKsXWNkMlz4DR4LfOk7O8CEg1dpLu/s1600/mes.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="572" data-original-width="1029" height="354" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJzbvyjfFgjD-_vSLUB5aXDiE5uJ9XGDTvgdRSu4gFyQksn0y0_1QwTpQnOLt7YsTDmvxHpLhEYrZ_H_4HhYKSlOSRP9c7j2fc-tlWG-Q3bCiKt0SvKsXWNkMlz4DR4LfOk7O8CEg1dpLu/s640/mes.png" width="640" /></a></div>
150 sampled progressive passes made by Messi during the Champions League group stage have both an expected completion probability and an attached improvement in non shot expected goals should the pass be completed. (NS xG is the likelihood that a goal results from that location on the field, it isn't the xG from a shot from that location).<br />
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If we simulate each attempt made by Mess 1,000's of times based on these average probabilities and the NS gain should the pass be completed, we get a range and likelihood of possible cumulative NS xG values.<br />
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The most likely outcome for an average player attempting Messi's passes is that they would add between 2.4 and 2.6 non shot expected goals to Barcelona's cause.<br />
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The reality for Messi was that he added 3.1 non shot expected goals.<br />
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There's around a 10% chance that an average player equals or betters Messi's actual tally in this small sample trial. But it is quantified evidence that Messi may well be a better than average passer of the football.Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-26044335215275311942019-01-07T09:05:00.001+00:002019-01-07T09:05:40.343+00:00Are Teams More Vulnerable After Scoring?One of the joys from the "pencil and paper" age of football analytics was spending days collecting data to disprove a well known bedrock fact from football's rich traditional history.<br />
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2-0 = dangerous lead has been a "laugh out loud" moment for those who went on more than gut instinct for decades.<br />
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Nowadays, you can crunch a million passes to build a "risk/reward" model and the only limitation is whether or not your laptop catches fire.<br />
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Myth busting (or not) perceived wisdom is now a less time consuming, but still enjoyable pastime.<br />
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Teams being more vulnerable immediately following a goal turned up on Twitter this week, although I've lost the link, so does it hold water?<br />
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Here's what I did.<br />
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Whether a team scores in the next 60 seconds depends on a couple of major parameters.<br />
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Firstly, a side's goal expectation.<br />
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Again not to be confused with expected goals, goal expectation is a term from the pre internet age of football analytics which is the average number of goals a side is expected to score based on venue, their scoring prowess and the defensive abilities of their opponent on the day.<br />
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Secondly, how long has elapsed.<br />
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Scoring tends to increase as the game progresses. <br />
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45% of goals on average arrive in the first half and 55% in the second. So if you want to predict how likely a side is to score based on their initial goal expectation, it will be smaller if you're looking at the 60 seconds between the 12th and 13 minute, compared to between the 78th and 79th.<br />
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Therefore, you take the pre game goal expectation for each team and when one team scores you work out the goal expectation per minute from this general decay rate for the other team over the next ten minutes.<br />
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Then you work out the likelihood that the "scored on" team scores in each 60 second segment via Poisson etc.<br />
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And then you compare that to reality.<br />
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The model doesn't "know" one team has just conceded, so if their opponents are really more likely to concede following their goal, the model's prediction will significantly under estimate the expected number of goals compared to reality.<br />
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There's a few wrinkles to iron out.<br />
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The first minute after conceding is going to be taken up with one team doing a fair bit of badge kissing and knee sliding, so it won't last for 60 seconds.<br />
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It's also going to be difficult to reply in the sixth minute after conceding if you opponent scores in the 94th minute and the ref has already blown for fulltime.<br />
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There's also the question of halftime crossover, where the 6th minute might actually be 21 minutes after the goal is conceded.<br />
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You can deal with these fairly easily.<br />
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I took time stamped Premier League date, ran the methodology and found <b>91</b> occasions where a side scored within ten minutes of conceding.<br />
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(I also split the ten minutes into 60 second segments, but I want to keep this short & more general).<br />
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From the model, in that timeframe, you would have expected those teams to score<b> , wait for it.......91</b> <b>goals</b>, based on when the goal was conceded, how good their attacking potential matched up to the opponent's defensive abilities and allowing for truncated opportunity at the end of the game & through celebration.<br />
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There's no need to invoke scoring team complacency or a conceding teams wrath to end up with the scoring feats achieved, at least in the sample of Premier League games I used.<br />
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Are Teams Vulnerable After Scoring?<br />
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Probably not.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0tag:blogger.com,1999:blog-6059983310325678283.post-81902502916414770122019-01-05T12:26:00.000+00:002019-01-05T12:26:33.375+00:00xG TablesThere's been a lot of interest on Twitter in deriving tables from expected goals generated in matches that have already been played out.<br />
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Average expected points/goals/ are a useful, but inevitably flawed way to express over or under performance in reality compared to a host of simulated alternative outcomes.<br />
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Averages of course are themselves flawed, because you can drown in 3 inches........blah,blah.<br />
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Here's one way I try to take useful information from a simulated based approach using "after the fact" xG figures from matches already played, that may not be as Twitter friendly, but does add some context that averages omit.<br />
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If you have the xG that each side generated in a match, you can simulate the likely outcomes and score lines from that match by your method of choice.<br />
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A side who out xG'ed the opponent is usually also going to be the most likely winner, in reality and in cyberspace.<br />
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But sometimes Diouf will run 60 yards, stick your only chance through Joe Hart's legs, nick three points and everyone's happy.<br />
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It just won't happen very often, but it does sometimes and then the xG poor team get three points and the others get none.<br />
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Simulate each game played, add up the goals and points and you now have two tables.<br />
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One from this dimension and one that "might" have happened in the absence of games state and free will.<br />
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It's easy and most readily understood to then compare the points Stoke got in reality to the points the multiple Premier League winners got in this alternative reality.<br />
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But it might be better if instead we compared the relative positions and points of each team in this simulation to the reality of the table.<br />
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I do that and repeat the process for every one of the 1,000's of simulations using each side's actual points haul in relation to each of their 19 rivals as the over/under performing benchmark.<br />
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This is what the 2017/18 season looked like in May based on counting the number of times a side's actual position and points in the table relative to all others was better than a xG simulation.<br />
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Top two overperformed, 3rd and 5th did what was expected, 4th and 6th under performed in reality.<br />
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Only 15% of the time did the xG simulation throw up a Manchester City season long performance that out did their actual 2017/18 season.<br />
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The model might have under valued City's ability to take chances, prevent goals, they might have been lucky, for instance scoring late winners and conceding late penalties to teams who can't take penalties.<br />
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So when you come to evaluate City's 2018/19 chances, you may take away that they were flattered by their position, but concluded that the likely challengers were so far behind that they are still by far the most likely winners.<br />
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Man United, De Gea, obviously.<br />
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Liverpool, 4th but perhaps deserved better. Too far behind City to be a genuine title threat, unless they sort out the keeper & defence.<br />
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Burnley, score first, pack the defence and play a hot keeper, bound to work again.<br />
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Huddersfield, 16th was a buoyant bonus they didn't merit.<br />
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Relegated trio, Swansea, Stoke, pretty much got what they deserved, WBA, without actually watching them much last season, looked really hard done by. If you're going for the most likely bounce straight back team, it was the Baggies.<br />
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All of this comment was made in our pre season podcast.<br />
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You can use this approach for goals scored/allowed to see where the problems/regression/hot/cold might be running riot, plus simulations and xG are just one tool of many.<br />
<br />Mark Taylorhttp://www.blogger.com/profile/15514407542599931686noreply@blogger.com0