Applying Elo Ratings To Football - Part Three
Musings on Backing, Laying, Trading, Punting, In-Running and more on the Betting Exchanges and related items of interest in the wide world of sports investing
In part three of his series on applying Elo Ratings to football, Cassini takes us further, showing us how applying deeper data such as shots on goal can provide us with more accurate ratings.
In Part Two, I looked at one way in which Elo ratings could be improved by measuring the strength of a win based on winning margin. However, the low scoring nature of football means that the match result often does not reflect the performance of the teams.
We have all seen games where one team has dominated, only to lose 0-1 to a goal very much against the run of play. If you limit your input to this single figure, goals scored minus goals conceded, you risk entering less than accurate data into your ratings.
While it is true that Birmingham City did beat Chelsea 1-0 on November 20th, 2010 is it fair and reasonable to award 100% or 70% of the points available to them? You might think it is, and I would say that is your decision to make, but my take on it is to look behind the result and use some of the other data that is readily available these days.
When deciding what data I should include, my rule is that there is a correlation between the data and goals. For example, simple logic tells you that there is a relationship between shots, shots on target, and goals. 10 shots, of which 5 were on target, doesn’t necessarily mean that a team will score 2 goals, but for each league there are fairly consistent ratios which we can use.
Charles Reep: Incorporating Shots On Goal
Pioneering football statistician Charles Reep began his research in 1950 (at 3:50pm on 18 March while watching Swindon Town play Bristol Rovers to be precise) and discovered (among other things) "that over a number of seasons it appears that it takes 10 shots to get 1 goal (on average)".
This average will of course vary from season to season, by league and by team, but the important thing is that there is a correlation between shots, shots on target, and goals scored. A note here that some of this data has an element of subjectivity about it, and you will often see major differences in the statistics for the same game from individual observers.
Again, how much effort you want to put into this is a personal choice. Researching the leagues you are interested in will show there are differences, which you can incorporate if you wish, for example as of 2011, the EPL is more efficient at converting shots to goals than Serie A.
I would however caution against changing these parameters too frequently once you have determined reasonable values, with my preference being to use an average for the past three seasons. The soocerbythenumbers.com website often has some interesting articles on this subject, along the lines of this entry from January 2011:
"Recall that, over the long run, the goal to shot ratio tends to be around .111 - or 1 goal in 9 shots. Across the four big leagues, it's clear that Serie A has by far the lowest goal to shot ratio - that is, it takes Serie A teams systematically more shots to score goals than teams in the other leagues. So far this season, Serie A is at .085 or roughly 1 in 12 shots - a third lower than what is "normal" for the big leagues. In contrast, the other three leagues are around the historical average at .104 (La Liga), .117 (EPL), and .123 (Bundesliga). So spectators in the Bundesliga only have had to see their teams take 8 shots before scoring a goal, while those in Serie A have seen their teams take a full 50% more shots (12) before getting on the scoreboard."
This data is important because it allows you to enter more meaningful data into your calculations. Arsenal 2 Chelsea 1 is a start, but in my view, the data is made more valuable by entering the shots and shots-on-target figures also, so you now have for example Arsenal 2:5:12 Chelsea 1:8:19 - a set of numbers that might reasonably lead you to conclude that Chelsea were a little unlucky in that their goals scored were lower than might have been expected.
You can include other data too, although I have yet to see any evidence of correlation between free-kicks or yellow cards. Red cards can obviously be more significant, but you would want to factor in the amount of time remaining at the time of the dismissal. A headline of "10 man City see of United" might sound dramatic and sell newspapers, or draw clicks, but if the dismissal was in the 90th minute, it's a little misleading to say the least.
In Part Four, I'll look at corner kicks and whether this additional data should be included in your Elo based ratings.
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And visit Cassini's blog : GreenAllOver.blogspot
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Do you think you could make a tutorial for how to construct such a system using Excel? It's just that I understand the concept, I know where to get the data from but I can't seem to manage it as my brain wants it.