Is Shot Blocking A Talent ?

The gradual availability of data relating to events that occur during a match has allowed for a more detailed examination of the ways in which goals are both scored and prevented. Save percentages for keepers are now commonplace and increasingly shots, the precursors to goals can be broken down into sub categories such as on target and off target efforts and also blocked shots. Distance from goal and pitch co ordinates are still  a relatively scarce resource, so at the moment the most productive approach relies on the use of accumulated shot data.

Blocked shots are probably the most neglected of the readily available data, but they account for the fate of almost a quarter of football's goal attempts and can shed light on the choices being made by strikers and the skill sets that may be possessed by defenders.

On average an EPL team will face or attempt over 500 goal attempts over the course of a Premiership season, but even these numbers are mere samples of each team's likely true ability. The actual percentage of shots that either a defence blocks or an attack has blocked over a season will be made up of a combination of randomly blocked efforts and possibly occasions where the skill of the shooter or the blocker has influenced the outcome.

So the first question we need to answer concerns whether blocking shots from a defensive perspective or avoiding blocks from an attacking one is likely to be a repeatable team talent. We can do this by assuming that the league average rate for blocks is shared by every team and then construct a typical spread of rates for blocked shots under these conditions of parity of "talent". If the spread we actually see deviates from that expected by pure chance we can deduce that other factors are present, either individual player talent within teams or a tactical approach that encourages blocks.

Based on the team records for blocked shots from the MCFC lite dataset, the raw attacking and defensive rates appear to indicate the spread isn't consistent with blocking of shots being a purely random process. Some teams possess qualities or setups that see them block or avoid blocks at rates that suggest a skill is involved, especially on the defensive side of the ball.

The Percentage Of Blocked Shots For Attacking Sides In The EPL 2011/12.

Team.	Raw Blocking %.	Regressed %.
Liverpool.	22.8	24.6
Chelsea.	23.6	25.0
Stoke.	22.9	25.1
Norwich.	23.5	25.2
Aston Villa.	24.2	25.6
Arsenal.	25.9	26.3
Everton.	26.7	26.7
Wigan.	26.6	26.6
Wolves.	27.1	26.7
Snderland.	27.3	26.9
Man Utd.	27.2	27.0
Newcastle.	27.4	27.0
WBA.	27.6	27.1
Spurs.	27.5	27.1
Blackburn.	27.8	27.2
Fulham.	27.7	27.2
QPR.	28.6	27.6
Swansea.	29.0	27.7
Man City.	28.7	27.8
Bolton.	30.7	28.5

We can also use these results to regress the extreme performers, both good and bad towards the league average based on the number of shots they faced or attempted. If the skill is repeatable the season on season correlation is much more likely to be seen in these regressed figures. The first table shows how good teams were at avoiding seeing their shots blocked and there appears to be little correlation between this talent an final league position.

Liverpool top the chart although their striking woes were well documented and successful sides such as Chelsea and Arsenal follow them home along with lesser lights such as Norwich and Villa. That's not of course to imply that a marginally improved ability to avoid seeing you shoot blocked isn't unimportant. Villa for example may owe their Premiership survival to the couple of extra shots that made their way through a forest of legs to possibly find the target. A team's overall record, good or bad is a product of a wide range of footballing talents of which shot blocking is just one.

The Percentage Of Shots Blocked By The Defence In The EPL 2011/12.

Team.	Raw Blocking %.	Regressed %.
Sunderland.	31.3	30.0
Everton.	31.5	29.9
Stoke.	30.1	29.0
Aston Villa.	29.9	29.0
QPR.	29.5	28.7
WBA.	27.7	27.4
Man Utd.	27.5	27.2
Man City.	27.5	27.1
Spurs.	26.7	26.7
Liverpool.	25.9	26.2
Newcastle.	25.4	25.8
Bolton.	25.5	25.8
Norwich.	25.4	25.8
Swansea.	25.0	25.5
Fulham.	24.9	25.4
Arsenal.	24.2	25.1
Blackburn.	24.1	24.8
Wolves.	24.1	24.8
Wigan.	23.5	24.5
Chelsea.	23.2	24.4

Defensive blocking ability appears to be more unevenly spread between teams. This may be because so teams have exceptional blockers or they adopt a packed defensive style or frustrate opponents into attempting more long range efforts that are more likely to be blocked. Blocking rates for individual players are currently impossible to calculate because although numbers of blocks made by players are available we can only guess at how many attempted blocks they were involved in. So rating individuals at the moment will have to rely on raw counting stats.

Stoke's three best blockers of a shot line up for duty in a defensive wall, while Ric & Crouchie seem less keen.

Stoke are once again towards the top of the table in terms of effectively blocking an opponents efforts and their style of play in 2011/12 certainly involved getting bodies behind the ball. Their three most prolific shot blockers, based on raw numbers, Whelan, Shawcross and Huth invariably made up part of a defensive wall. Of course they may have combined to blocked many efforts precisely because they were often used in the wall or they may have been used in the wall because they had shown great aptitude at anticipating and blocking shots...........

Red Cards, Opening Goals & Wins For Both Manchester Clubs.

Last year's title race was fought out almost exclusively between the two Manchester clubs and their matches following the International break saw the Red and Blue half each sent off as strong favourites to beat two Midlands sides, Stoke and WBA, respectively.

United had the more straightforward task, taking on a Stoke side who are now permanently shorn of their extraordinarily long throw weapon and mid table will remain the height of their ambitious as they gradually try to wean themselves off an over reliance on set play goals.

A cursory glance at the EPL table indicated that City's game could be far from straightforward. Their hosts, WBA had started the season strongly with four wins and a solitary defeat and at the start of the day they lay  a point behind City. However, just as the very best can occasionally produce an atypically poor run of short term results, a team that is destined to end the season no better than mid table can also produce Champions League qualifying results before drifting back to their expected level of attainment. The game was certainly not priced up as a meeting of near equals.

It's not uncommon for traditionally mid table teams to start a season by showing markedly improved form, but as this guest post demonstrates few teams manage to maintain that improvement in the manner achieved by Newcastle last season.

Expected Points Graphs For Both Manchester Teams, 20 October, 2012.

Scorers.
11', Rooney(og), 0-1
27', Rooney, 1-1
44', van Persie, 2-1
46', Welbeck, 3-1
58', Kightly, 3-2
65', Rooney, 4-2

Scorers.

23', Red Card, Milner (Manchester City).
67', Long, 1-0
81', Dzeko, 1-1
92', Dzeko, 1-2

So pregame, despite the hot start to the season from WBA, we had two genuine title contenders taking on two mid table teams with United enjoying the added bonus of home advantage and two hours later both Manchester clubs had each claimed maximum points from their matches.

United were the first Manchester club to suffer a setback after 11 minutes, when Rooney lost Shawcross while defending an Adam freekick and tamely walked the ball into his own net as he tried to recover his position. The first goal is often a significant event in a game, however as the post at 5 Added Minutes shows
the opening strike must also be put into a proper context.

United started the game as strong favourites, the game was barely ten minutes old, so they had the lions share of the match to turn things around. At the start of the match the most likely scoreline was a 2-0 victory for the hosts and in the short time that had elapsed, the relative team strengths and the likely shift in game dynamics towards a more attacking United approach made that outcome still most likely. So despite enjoying the ideal start, Stoke were still very much the junior partners in this matchup. And 80 playing minutes later they found themselves on the wrong end of a 4-2 defeat.

If United suffered a minor setback, their rivals City had two punishing obstacles to overcome. Not only did they also concede the opening goal, they did so much later in the game. But they also compounded their plight with Milner's red card after 23'.

A red card on average is bad news. Individual teams can occasionally overcome the disadvantage to secure a positive result, but the general trend is for the carded side to score less than they would have done with a full complement of players and concede more. The severity of the effect is obviously time dependent as well and the graph below illustrates the average depletion in goal difference seen by a team playing with just ten men for varying lengths of time.

The Effect Of A Red Card In The EPL.

Milner's red card, coming as it did after only 23 minutes cost City just over a whole goal. Unlike United, City found the equivalent of allowing the first goal, albeit by way of a red card and the away venue too much of a setback to maintain favouritism in the game and when they further conceded the first goal in reality after 67 minutes, their chances of taking anything from the match was remote. They had to overcome two of the most damaging occurrences in football.

Anyone merely looking at the final score would have seen a not unexpected result, a City win by one goal was the most likely prematch outcome, but as the expected points graph for the game demonstrates, Dzeko provided City with a victory of epic proportions.

Using Scoring Records To Predict Future Performance.

In a conversation last week with Simon Gleave of "Scoreboard Journalism" fame I was reminded of the pivotal role that the Poisson Distribution has played in explaining a team's win, loss and draw tally from their goal scoring record and also in predicting their future results from their presumed goal expectancy in those subsequent games. As a retrospective tool the Poisson is unspectacular, but in a predictive role, it's simplicity of use and the ability it gives us to shed light onto the nature of a team's talent and ability is unsurpassed.

There is no shortage of football based posts which describe the actual use and limitations of the Poisson, so I will direct readers to such posts as this one. But for brevity, the Poisson allows you to model the likelihood of any number of discrete events occurring given that we know the average rate at which these events are likely to occur.

So if we think a team is going to average 1.4 goals per 90 minutes against a particular defence we can estimate the probabilities of that team scoring exactly 0, 1, 2, 3 and so on goals. If we repeat the process for their matchday rivals, this allows us to move onto the prediction of assorted scorelines and ultimately game results.

The mathematical steps involved in producing goal probabilities is fairly straightforward, but it is the calculation of each team's future expected goal scoring and conceding rates where much of the hard work lies. Manchester United, as next Saturday will probably confirm score goals at a much higher rate than Stoke City. Before we can confidently begin to estimate the rate at which United will score against City, we need to incorporate such obvious variables as United's scoring rate over a period of games, Stoke's rate of conceding, together will less obvious figures such as the general rate at which home teams outscore away sides and the level of goalscoring typically seen within the Premiership.

By combining such rates for each team we can begin to estimate the likelihood of such match outcomes as a United victory and other, much less likely occurrences, such as a Stoke City away win. How we decide which rates to use for each team will depend on how we implement adjustments such as the weight we give to more recent matches, how severely we regress our final figures towards the mean of our choice and, in the case of the Newcastle Sunderland game whether or not the game is a derby match. (Such matches tend to throw up lower scoring, more evenly matched games).

But the most influential choice we will need to make will be how many matches we use to derive our average team scoring rates. In the table below I've calculated the chance of each EPL team winning their games over the coming weekend using a Poisson based approach incorporating the scoring averages of teams calculated over the previous 32 home and away games, the previous 20 and just using the 7 games played so far this season.

The Win% Chances For EPL Sides On Saturday Using A Poisson Calculation & Expected Scoring Rates Over Differing Timescales.

 

Team.	Using Last 32 Games.	Using Last 20 Games.	Using Last 7 Games.	"True" Odds.
Man Utd.	75	70	54	76
Stoke	8	9	18	8
Fulham.	60	71	80	53
A Villa.	16	9	7	21
Norwich.	15	9	4	15
Arsenal.	65	75	86	62
QPR.	19	15	8	29
Everton.	57	66	79	43
Sunderland.	45	26	36	38
Newcastle.	28	44	30	32
Swansea.	54	42	72	48
Wigan.	21	32	12	24
Spurs.	44	32	21	37
Chelsea.	29	41	52	34
WBA.	22	23	40	19
Man City.	52	52	34	57

The team specific inputs used for each team in Saturday's matches is merely the average of the goals that were scored or conceded by teams over the three different timescales and the purpose of the exercise is to see which timescale produces the most reliable estimation for pre game win and loss probabilities. in short, is it better to use much more recent, but smaller samples of a team's attacking and defensive ability or is more information contained in older, but more numerous sample sizes.

I've used the bookmakers win odds for each team as the "true" odds comparison. These figures are a great untapped source of information. Whatever your views on gambling, the odds presented by bookmakers, once the overround has been stripped from the prices give you an incredibly accurate estimation of the true odds of an event occurring. The odds compilers have access to large amounts of data, long experience at setting prices and a huge vested interest in producing accurate odds. Also we are not at a stage of the season when prices are particularly skewed by expected weight of money. Last season, Bolton's final "must win" visit to Stoke saw the relegation threatened side priced up as having around a 33% chance of winning, even though the evidence suggested that 22% was a more realistic estimate.

Using readily available bookmaking odds as a benchmark does away with the need to produce the copious amounts of estimations that are needed to evaluate a model's effectiveness by comparing predictions against actual outcomes. A fifth of the way into the year and if your win estimate greatly varies from the general bookmaking consensus, then it is your model of events that is almost certainly the one with the flaws.

Swansea and Stoke are likely to experience very different results on Saturday.

Our naive model has no whistles and bells, but the results are overwhelmingly in favour of taking into account as much goalscoring data as possible, even at the expense of recency. Win predictions produced via the Poisson process using data going back 32 games were closest or joint closest to the bookmakers estimate in 13 of the 16 cases. They are shown in blue. (I've omitted the matches involving promoted sides, because they require separate adjustments based around the promoted sides scoring and conceding records from the Championship).

20 game estimates were top or joint top in three out of the 16 teams and the most recent data from the seven games so far this season won out for just Newcastle and Sunderland. A local derby where depressed scoring and home advantage is always factored into prices, so even this meagre victory for recent form alone was a hollow one.

So of the three choices, the scoring records over the previous 32 games trounces the two alternatives as the best predictor of performance in the future. Eight matches comprising 16 teams isn't a huge test sample, but the findings are confirmed over many more matches and multiple leagues and seasons.

Teams can often produce short term bursts of atypical results in a sport where team scoring very rarely averages much more than two goals a game and more is certainly better if you chose to evaluate teams by goals scored and allowed, even if that means you are going back to matches played early in a previous season.