AFL Player Correlations

Player Correlations


Viewing Correlations for Tom Mitchell
Mitch Lewis2175.001.000
Dallas Willsmore2110.001.000
Conor Glass9289.790.726
Jonathon Ceglar10398.570.601
David Mirra5261.600.523
Blake Hardwick38187.190.400
Paul Puopolo31205.970.354
Teia Miles10207.830.353
Kurt Heatherley460.500.347
James Worpel7195.500.324
Ryan Schoenmakers17103.610.281
Will Langford25122.000.253
Ben Stratton2890.980.166
Jonathan ORourke3101.330.136
Taylor Duryea3160.040.128
Jaeger OMeara2461.030.092
Ben McEvoy3718.690.036
Ryan Burton3814.950.030
Jack Gunston41-0.180.000
Isaac Smith42-18.69-0.035
Harry Morrison18-26.23-0.043
Brendan Whitecross14-48.00-0.107
Daniel Howe33-66.35-0.114
Liam Shiels40-76.90-0.120
Tim OBrien28-90.13-0.137
Ricky Henderson37-75.27-0.158
James Frawley25-74.84-0.181
Kaiden Brand25-60.85-0.186
Luke Breust41-160.99-0.212
Jarryd Roughead41-120.01-0.237
James Sicily33-221.81-0.286
Shaun Burgoyne34-119.79-0.306
Jarman Impey20-168.74-0.318
Cyril Rioli10-96.89-0.573
James Cousins6-200.00-0.718
Grant Birchall4-63.08-0.767

Correlation is used to define a relationship between two variables, with a perfect positive correlation indicated by 1.0 and a perfect negative correlation being assigned a value of -1.0. Correlation however does not indicate the extent to which the two variables change together. Because of this, it’s important to understand the co-variance between the two variables also.

The chart above shows 4 theoretical player’s scores over 6 games. Each of Player’s B, C & D have high correlations to Player A indicating they have a perfect (or near perfect) positive correlation. However, it’s clear from the chart that if you were expecting Player A to have a high scoring game, the best player to stack him with would be Player D as he scores significantly better than the others when Player A scores higher. Correlation does not give us any insight into this and it’s by understanding co-variance that we can take advantage of this.

Player Correlation Co-Variance
Player B 1.000 350
Player C 1.000 438
Player D 0.980 980

Looking at the numbers we can see that Player D’s correlation is in fact slightly less than the other players, however, his co-variance is much larger. In simple terms, the co-variance indicates how two variables vary together and as such for fantasy sports the larger the value the better.

So, we can see from this simple example that correlation should always be used in conjunction with co-variance as correlation is only half the story.