Top club stats of 2014 and the 2014-15 Premier League and Euro season so far

NB: Europe refers to the top five leagues — English Premier League, Spanish La Liga, German Bundesliga, Italian Serie A and French Ligue 1
Player stats will be published on Dec. 31.
POINTS
Most points in Premier League in 2014:
Man City 88 (+56… Read more ›

Restarting fast and slow: Analyzing restart behavior in 2014 J-League

Last year I wrote a series of posts on effective playing time in the top two J-League divisions at the mid-season and end-of-season marks.  Further analysis of the numbers using an admittedly crude regression model indicated that some clubs have a significantly greater effect on the amount of playing time than others, for better or […] Read more ›

Europa League seedings do not separate best and worst

Due to basing the seedings for the Europa League draws on six matches within uneven groupings which were created by the UEFA coefficient systems, the seedings for today’s Europa League draw do not actually separate the best and worst remaining … Continue reading Read more ›

Weighted Shots v Unweighted Shots As A Predictor of Future Goal Difference in the EPL.

Tom Tango has recently presented an alternative to Corsi in hockey that weights shots differently depending on whether they resulted in goals, saves, misses or blocks.

One of the logical tests of the new metric is see how well it correlates to useful team information, such as future goal difference, compared to projecting from previously used metrics, such as unweighted shot differential or ratios.

The expectation voiced in many hockey circles was that because the “Tango” correlated almost perfectly to the traditional Corsi metric, the added information hoped for by weighting different types of shots would be negligible, at best.

In a typical concise and insightful post, here, Tango addresses the issue of the virtually perfect correlation between both metrics. Pointing out that using basic shot data from identical samples to test the correlation to out of sample data, such as future goal difference, gave different coefficients of correlation depending on whether the Corsi or Tango was used.

In short, weighted shots showed higher r values, despite the strong correlation between the two metrics.

 r Values for Weighted & Unweighted Shot Differential and Ratios when Correlating to Future Premiership Goal Difference.

After X Games r for TSR r for Shot Differential. r for Weighted Shot Differential
2 0.49 0.51 0.57
6 0.70 0.71 0.77
10 0.70 0.71 0.76
15 0.74 0.74 0.80
18 0.73 0.74 0.80
20 0.73 0.74 0.79
24 0.72 0.73 0.77
30 0.65 0.66 0.69
34 0.55 0.55 0.56

Tango’s defence of his new metric can be summed up in this extract from the linked post.

“But more amazing is that even though the correlation of Corsi to Tango (both based on the same samples) was close to r=1, when we correlate each to out-of-sample data (in this case, goal differential from OTHER games), Tango correlated at r=.50, while Corsi was r=.44.  Or if you prefer r-squared, it’s .25 to .19, respectively.”

I have therefore repeated the exercise for the Premiership, using three flavours of shot based metrics in one part of the season and testing the correlation between these at an individual team level and goal difference for teams in the remainder of the season.

And the weighting of shots also appears to make a difference in soccer as well as in hockey. Correlation peaks around mid-season, but at every stage, weighting proved a superior correlation to goal difference in the remainder of the season compared to unweighting.

It also makes intuitive sense to reflect the extra information present in a goal compared to just a shot.

Read more ›

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