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Saturday, 18 February 2017

Expected Saves Ageing Curve.

Everyone is probably familiar with the concept of expected goals, assists and saves by now.

A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or her actual output.

A player who scores say ten goals against a cumulative expected goals tally of eight is therefore considered to have over performed against their expectation.

The reasons for and the sustainablility of this over achievement can be  many and varied, ranging from the presumption that they are a persistently skilled finisher, they have had a hot, finishing run or the model is inadequate to fully describe the nuances of real football life. (Although the latter may be mitigated by running goodness of fit tests on out of sample data).

Instead of merely presenting expected and actual goal numbers ranked by over and under achievement, the same information can be presented in a more graphical form.

Rather than quoting cumulative figures, the granular nature of attempts is respected by using a Monte Carlo simulation for all shots and headers to produce a range and frequency of potential actual goals scored based on all attempts and these distributions are then compared to reality.


Here's a recent example that shows Chelsea and to a lesser degree, Spurs and Arsenal outstripping their simulated range of potential goal difference tallies based on the number and quality of chances they each have created and allowed in a possibly unsustainable manner.

The same approach may be used to describe, if not fully predict the performances of goal keepers.

In defining the difficulty of the task faced by a keeper it is legitimate to include post shot information, such as placement, strength and whether or not a shot took a deflection. These are additions that may not be repeatable from the shooter's point of view, but do better describe the reality of the keeper's task.


Here's a distribution plot for a number of Premier League goalies in 2016/17. Hull's Jakupovic's is most likely to have conceded 15 goals, rather than the nine he actually has and it is around a 1% chance that the average keeper described by the model would have performed as well or better.

By contrast Bravo is having a well documented torrid time at Manchester City, conceding nine more goals that the most likely peak of the simulated distribution of the attempts he has been asked to save.

However, the question remains as to whether these snapshots of "form" represent a longer term up or down tick in the keeper's potential future performance in his current environment or if they will regress towards less extreme levels going forward.

David de Gea is a couple of goals in credit against the model's expectation in 2016/17 and while this is not uncommon for United's keeper, it is possible to find runs of 50 attempts when would have been classed as under performing.



Notably in May of 2015 and February 2016.

Perhaps most usefully, this simulation approach may open up another way to look at the age at which a position generally reaches the peak of a particular attribute.

A variety of methods and curves have been used, See here, here and here. Grouping keepers by their rounded age when they did or didn't make a particular save and then seeing if this enlarged group of ages show a tendency to over or under perform may be another route.


Here's the under (red) and over (green) shot stopping performance of Premier League goal keepers, sorted by age over multiple seasons.

Notwithstanding the problems of survivor bias for older keepers in this type of traditional plot, there does appear to be tendency for keepers to over perform an attempt based model in their mid to late 20's, peaking at around 28 (which is consistent with other approaches listed above).

Their under performance relative to their older selves in their formative years and in the advanced stages of their careers compared to their younger selves is also typical of ageing curves in general.

This approach of course may be used for other performance related indicators across other playing positions.

Modelled data via InfoGolApp

Wednesday, 4 January 2017

How Dominant Are Chelsea's Halftime Record Chasers?

Chelsea travel to Spurs tonight needing a win to equal the record for the number of consecutive Premier League wins.

After 19 games, half a season, they have accumulated 49 points, beaten only by the 2005/06 Chelsea side, who gained 52 points and equalled by the 2003/04 Manchester United team.

In keeping with all of the traditional title challengers in the Premier League, Chelsea has put a lacklustre 2015/16 behind them and improved their expected goals at both ends of the pitch as the season has progressed.


It is an impressive reversal of fortunes, but it is also shared by their title challengers. Only Arsenal has shown a marked decline in their defensive metrics and of course Leicester, although the Foxes have been replaced by a resurgent Manchester United.

Chelsea are therefore worthy favourites to regain the title in May 2017. In simulations of the remaining matches, they are odds on to finish top of the pile.

But where does the current halfway house, Chelsea lie in the Premier League role of honour?

Points won is a natural starting point, but that neglects to account for the closeness and quality of challengers.

A better measure is the points per game won by Chelsea, expressed as a standard score, which attempts to account for how dominant a side has been using the characteristics of this particular season as a benchmark.


Chelsea (2016/17) is currently 2,06 standard deviations above the league average points per game prior to last night's results. Five teams are within 10 or fewer points of their current total, albeit after one game more, with the exception of their opponents tonight, Spurs.

By contrast, 2014/15 Chelsea had three fewer points than the current team, but had burned of a lot of challengers, with the exception of Manchester City. So arguably that was a more dominant mid term performance.

Similar comments apply to the other eight teams above Chelsea in the preceding table in terms of standard scores at halfway.

Check out the ultimate performance of the league leaders on Christmas Day based on their standard scores in this post from 2014

Monday, 26 December 2016

Palace's Pre Christmas Expected Goals Breakdown.

This time last year, Palace were 6th in the Premier League and the Europa League was being touted as a legitimate aim. 

This time around they're 17th and have embarked upon Sam Allardyce's return to domestic football after his unbeaten reign as England manager.

                   Palace's ExpG Breakdown for their First 17 Games in 2015/16 and 2016/17.



Some small sample sized bulges have appeared in the way they've dealt with corners and set pieces and the post kick quality of the shots or headers (in grey) have been less kind in 2016/17 than they were in 2015/16, but overall the cumulative expected goals are broadly similar for both periods.

Randomness partly made Pardew a hero in 2015/16 and unemployed a year later.