"Goal expectation", calculated from a weighted and smoothed average from a side's actual number of goals from their last x number of matches, was often the raw material to use to work out the chances of Premier League high flyers, Leeds beating mid table Tottenham.
Shot numbers (which included headers) then became the new ingredient to throw into the mix and a team's shooting efficiency quickly became a go to stat.
Multi stage precursors to goal expectation models where further developed when shot data became available which was broken down into blocks, misses and on target attempts.
To score, a side had to avoid having their shots blocked, then get them on target and finally beat David James.
This new data allowed you to attach team specific probabilities to each stage of progression towards a goal and arrive at a probabilistic estimate of a team's conversion rate per attempt.
Unlike today's xG number, the figure told you nothing specific about a single shot, nor was it particularly useful in helping to describe the outcome of a single game, even with double digit attempts.
Aggregated over a larger series of matches by necessity, this nuanced conversion rate, that included information about a side's ability to avoid blocks, get their efforts on target and thereafter into the goal, allowed you to deduce something about a side's preferred attacking and defensive style.
Also if that preference persisted over seasons, this team specific conversion rate could be used alongside each team's raw shot count in the recent past to create novel, up to date and hopefully predictive set of defensive and attacking performance ratings.
Paper and pencil only lasts slightly longer than today's hard drive, so unfortunately I don't have any "goal expectation" figures for Liverpool circa 2002.
However, with the additional, detailed data from 2017, I decided to re-run these turn of the century, slightly flawed goal expectation models to see if these old school, team specific conversion rates offer anything in today's more data rich climate.
To distinguish them from today's xG I've re named the output as "chance quality".
Chance quality is an averaged likelihood that a side would negotiate the three stages needed to score.
Arsenal had the highest average chance quality per attempt in 2015/16.
The Gunners were amongst the most likely to avoid having their attempts blocked, those that weren't blocked were most likely to be on target and those that were on target were most likely to result in a goal.
Leicester, in their title winning season also created high quality chances per attempt, but Tottenham appeared to opt for quantity verses quality. They were mid table for avoiding blocks and finding their target, but their on target attempts were, on average among the least likely to result in a goal.
Only Palace of the surviving sides were less likely to score with an on target attempt than Spurs.
Here's the same chance quality per attempt, but for attempts allowed, rather than created by the non relegated teams from the 2015/16 season.
The final two columns compare the estimated goal totals for each team using their shot count in that season and their conversion, chance quality from the previous year, to their actual values.
The thinking back in 2000 was that conversion rate from a previous season remained fairly consistent into the next season and so multiplying a side's chance quality by the number of shots they subsequently took or allowed would give a less statistically noisy estimate of their true scoring abilities.
Here's the correlation between the estimated and actual totals using chance quality from 2015/16 and shot numbers from 2016/17 to predict actual goals from 2016/17.
There does appear to be a correlation between average chance quality in a previous year, attempts made the next season and actual goals scored or allowed.
The correlation is stronger on the defensive side of the ball, perhaps suggesting less tinkering with the back 3, 4 or 5.
With full match video extremely rare in 2000, it might have been tempting to assume chance quality had remained relatively similar for most sides and any discrepancy between actual and predicted was largely a product of randomness.
Fortunately, greater access to granular data, availability of extensive match highlights and Pulisball, as a primitive benchmark for tactical extremes, has made it easier to recognise that tactical approaches and chance quality often varies, particularly if there is managerial change.
In this post I compared the distribution of xG for Stoke under Pulis' iron grip (fewer, but high chance quality attempts) and his successor Mark Hughes (higher attempt volumes, but lower quality attempts).
Subsequently, under Hughes, Stoke have tended to morph towards the Hughes ideal and away from Pulis' more occasional six yard box offensive free for all.
So a change of manager could lead a a genuine increase or decrease in average chance quality, which in turn might well alter a side's number of attempts. And any use of an updated version of chance quality should come with this important caveat.
For anyone who wants to party like it's 1999, here's the average chance quality per attempt from the 2016/17 season using this pre-Twitter methodology allied to present day location and shot type information.
Use them as a decent multiplier along with shot counts to produce a proxy for the more detailed cumulative xG now available during the upcoming season or as a new data point to assist in describing a side's tactical evolution across seasons.
In 2016/17, Crystal Palace improved their chance quality compared to 2015/16 with half a season of Allardyce and Arsenal maintained their reputation for trying to walk the ball into the net.
All data is from infogolApp, where 2017 expected goals are used to predict and rate the performance of teams in a variety of leagues and competitions.
No comments:
Post a Comment