## The IPL player auction

Sorry for the delay in updating — I've just got back from a short holiday in Amsterdam. On Tuesday morning I'm heading off to Florence and Rome, so there'll be another break in posting soon.

I had a question on whether Indian players were valued more than non-Indians in the IPL auction. The answer is that they were, by about \$250k each. My analysis is a bit rough, since I didn't want to get bogged down in details in the couple of days I have before Italy.

Firstly, it's important to note that it's not a free market — there were requirements on young players, international players, icon players would have distorted the market, etc. But we'll see what the numbers tell us.

I took all the non-icon players who had ODI stats (or, failing that, List A stats) that included batting strike rate. Because it seemed a reasonable thing to do, I gave each player a batting rating, defined as the batting average multiplied by the strike rate, divided by 100, divided by 20 (roughly). For bowlers (and I chose bowlers by looking at them and deciding whether I'd consider their bowling in buying them; there's a grey area of course, but for most players it's pretty obvious) I gave a bowling rating: bowling average times economy rate, divided by 6, divided by 25.

I might be biasing the ratings towards batsmen or towards bowlers, but it shouldn't be too bad. Then I added the batting and bowling ratings for an overall player rating.

I put three other variables into the regression model: number of matches (a bit dodgy in one or two cases, where I used List A rather than ODI's), and dummy variables for Indians and wicket-keepers.

I probably should have done something about the Australians and West Indians, who are only available for half the tournament, but I couldn't be bothered.

Here are the results of the regression:
`Modèle 1: Estimation en MCO avec 70 observations 1-70Variable dépendante: salary      VARIABLE       COEFFICIENT        ERR. STD         T           p. critique  const             46927,8         114520             0,410   0,68332  mat                 673,801          361,921         1,862   0,06716 *  rating           163109            62931,6           2,592   0,01178 **  indian           267326            71927,6           3,717   0,00042 ***  keeper           136852            93199,5           1,468   0,14682  Moyenne de la variable dépendante = 504357  Écart-type de la var. dép. = 286130  Somme des carrés des résidus = 4,25589e+012  Erreur standard des résidus = 255881  R2 non-ajusté = 0,246619`

Key points:

- There is a slight positive correlation between matches (i.e., experience) and salary. For every hundred extra ODI's, the salary goes up by about \$65000.

- My hastily calculated player ratings are positively correlated with salary. Increase the batting average (times strike rate) by 10, your salary goes up by \$80000.

- If you're Indian, you get a bonus \$265000. Indian cricketers can expect to be part of marketing campaigns.

- Wicket-keepers get an extra \$135000, and I'll ignore the p value which tells me that it's not significant. The extra money they get is expected, since I didn't incorporate wicket-keeping skills into the player ratings.

- These factors explain 25% of the statistical variance, which is 50% of the salaries in cricket terms.

Now just for a bit of fun, I decided to use the player ratings to work out how many dollars each team spent per player rating point. I've fixed it so that the teams are on a scale of 3 to 9, so that I can compare with Q.
`Team        Me   QJaipur      9    3Chennai     6,3  7Mumbai      3    6Bangalore   5,3  5Hyderabad   5,8  8Mohali      3,1  7Kolkata     3,5  9Delhi       6,0  9`

The conclusion here is that at least one of me and Q has no idea what we're doing. Of course, my analysis is based purely on ODI numbers (possibly out of date — several people have said that T20 is a young man's game, with the play very fast), whereas Q's looked at T20 form and crowd-drawing power. Even so! I suspect the difference of our ratings of Jaipur is that they didn't actually spend much money on players. So they got quality for what they spent, but the overall team isn't all that good. The point of the bidding process is to get the best team (including marketing, etc.), not to get the most player rating points per dollar.

I just saw this now when I came onto your blog to link it to one of my posts :-)

I'm sure you have done a very smart mathematical / statistical model but I don't understand why you got a rating of 9 for Jaipur, which you yourself mention is a weak team.

And how do you get such a low rating for Kolkata, which is one of the strongest teams on paper?

Chennai and Bangalore are something we both seem to agree on..

Then again as you mentioned, you have done a quantitative analysis to rate the teams, while mine was based on my observation about player form and crowd pulling power without any calculations.

Good work David :-)

excellent I was checking for people who have used regression models in sports and came across your blog.

In hindsight your model has done pretty well!