I’ve been working on spreadsheets of the value of every player for every EPL team, and it’s FINALLY done1 If you’re interested in the method you can read more at http://soccer.chadmurphy.org/methods/arsenals-room-to-improve/, but the quick version is that I calculate the number of points each team is expected to earn using a Random Forest model. Then I replace their striker with each striker in my database, re-running the Random Forest and calculating the new expected points. I sort them by number of expected points, and listed the top striker in this plot and plotted the number of points the team would be expected to gain with that striker. The plot is available below:
There are a couple of interesting things to note: first is the relatively low number of points each new striker would add to each of these teams. Theo Walcott would be a huge upgrade to Sunderland and Spurs, but the average gain is only 7 points. This could mean two things: most teams already have a fairly good striker, or strikers are generally overvalued in terms of their contribution to the team. Or maybe I’m undervaluing how much 7 points is for one team to improve based on signing one player.
The second thing is how popular Theo Walcott is in this model. He’s the best striking option for 7 of the 20 EPL teams, which is remarkable considering he’s not considered a top tier elite striker. Lionel Messi was the next common best option, and was almost always in the top 10 choices for the rest of the teams, giving me a nice validity check on my model. Bournemouth’s Glenn Murray was also really popular, which was surprising, although Messi was considered an upgrade over him for Bournemouth.
This was an interesting exercise, and is the first check on the way to my “Points Above Replacement” measure (PAR). Thinking about doing defenders next.
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- Each team takes ~300 minutes to run. Multiply that times 20 teams plus a couple extras for coding errors along the way/accidentally deleting files and just generally being sloppy… ↩