Variables used

The variables used were across the 2013 and 2014 AFL season.

So something that could be an interesting extension of this, is because footywire has made some extra statistics available like meters gained, intercepts and tackles inside 50 to name a few. You could run the same model type that the paper used say logistic regression. But you could see if variables like meters gained, intercepts and tackles added any extra value. This is why this particular part of my blog exists, because its an interesting question and one that a fan of the game can hopefully answer online.

Other things to note from the paper is that its not the raw team total that is being used in the model, rather relative difference values between opposing teams for each performance indicators were used.

What I think this means is that instead of WCE totals kicked being used it was WCE total kicks minus Collingwoods total kicks being used.

The other thing to note, is that the paper only talks about home and away matches so we have to filter out finals

c("Variables used",
  "Kicks",
  "Marks",
  "Handballs",
  "Tackles",
  "Inside 50s",
  "Clearances",
  "Clangers",
  "Contested possessions",
  "Uncontested possessions",
  "Contested marks",
  "Marks inside 50",
  "Goal conversion",
  "Kick:handball ratio",
  "contested:uncontested possession ratio",
  "disposals",
  "hit outs",
  "Free kick differential")
##  [1] "Variables used"                        
##  [2] "Kicks"                                 
##  [3] "Marks"                                 
##  [4] "Handballs"                             
##  [5] "Tackles"                               
##  [6] "Inside 50s"                            
##  [7] "Clearances"                            
##  [8] "Clangers"                              
##  [9] "Contested possessions"                 
## [10] "Uncontested possessions"               
## [11] "Contested marks"                       
## [12] "Marks inside 50"                       
## [13] "Goal conversion"                       
## [14] "Kick:handball ratio"                   
## [15] "contested:uncontested possession ratio"
## [16] "disposals"                             
## [17] "hit outs"                              
## [18] "Free kick differential"

They then removed some variables from the model fitting process. For example Multicollinearity is a typical problem in regression. It happens when there is a relationship between your predictor variables. In this case we know that essentially kicks + handballs = disposals which would be an example of multicollinearity.

How to get the data ready

To fit the models described in the paper, we would need a dataset that contains the game information joined with a binary indicator 1 if the team won and 0 if the team lost (in the paper draws were removed).

To do this using fitzRoy would require joining fitzRoy::match_results onto fitzRoy::player_stats

library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.3.1     ✔ forcats 0.3.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(fitzRoy)
library(knitr)
library(broom)
df1<-fitzRoy::match_results
df2<-fitzRoy::player_stats

So once we have the box-score statistics from footywire, fitzRoy::player_stats we have to aggregate them to a match level. That is because the paper looked at team level statistics not player level.

So to do that using the tidyverse we first have to come up with at what level we want to summarise our data by (team and game). To do this we use group_by.

library(tidyverse)
teamdata<-df2%>%dplyr::select(-Player)%>%
  group_by(Date, Season, Round, Venue, Team, Opposition, Status, Match_id)%>%
  summarise_all(sum)%>%
  filter(Season %in% c("2013","2014"))%>%
         group_by(Date,Season, Round, Team, Status, Opposition, Venue, Match_id)%>%
         summarise_all(.funs = sum)%>%
         group_by(Match_id)%>%
         arrange(Match_id)%>%
         mutate_if(is.numeric, funs(difference=c(-diff(.), diff(.))))%>%
  filter(!Round %in% c("Qualifying Final","Elimination Final",
                       "Semi Final","Preliminary Final","Grand Final"  ))

After that we would want to join on the score dataset so we can come up with win/loss column.

dataset_scores1<-df1%>%dplyr::select (Date, Round, Home.Team, Home.Points,Game)
dataset_scores2<-df1%>%dplyr::select(Date, Round, Away.Team, Away.Points,Game)
#Sometimes when joining datasets together it helps to rename things for consistency

colnames(dataset_scores1)[3]<-"Team"
colnames(dataset_scores1)[4]<-"Points"
colnames(dataset_scores2)[3]<-"Team"
colnames(dataset_scores2)[4]<-"Points"

df5<-rbind(dataset_scores1,dataset_scores2)
dataset_margins<-df5%>%group_by(Game)%>%
  arrange(Game)%>%
  mutate(margin=c(-diff(Points),diff(Points)))
# View(dataset_margins) # I have commented this out, but always good to view 
dataset_margins$Date<-as.Date(dataset_margins$Date)

dataset_margins<-dataset_margins %>%mutate(Team = str_replace(Team, "Brisbane Lions", "Brisbane"))

dataset_margins<-dataset_margins %>%mutate(Team = str_replace(Team, "Footscray", "Western Bulldogs"))

complete_df<-left_join(teamdata,dataset_margins,by=c("Date"="Date",  "Team"="Team"))

The next step in the paper is feature creation. What this means is that we are creating some variables to be fed in the model. In this paper while it uses data available from fitzRoy it also has some variables created. These variables are:

library(knitr)
df<-data.frame(stringsAsFactors=FALSE,
   Feature.created = c("Goal Conversion", "Kick to handball ratio",
                       "Contested to uncontested possession ratio"),
       Description = c("the percentage of scoring events that were goals",
                       "the number of kicks compared to handballs expressed as a ratio",
                       "the number of contested possessions compared to uncontested posessessions expressed as a ratio"))
df%>% 
  kable("html", caption = "Table of Variables Created for Paper")

• goal conversion is an example of a variable that we might not be able to fully replicate. For example if the player has a shot on goal but kicks it out of bounds that isn’t recorded in fitzRoy.

So lets just go ahead and create them. In the tidyverse, this would be done using the mutate function.

dataset_model<-complete_df%>%filter(margin!=0)%>%
  mutate(goal_conversion=(G/(G+B)),
                     kicktohandball=K/HB,
                     Contestedtouncontested=CP/UP,
                     win_loss=ifelse(margin>0,1,0))%>%
  mutate(kicktohandball_diff=c(-diff(kicktohandball),diff(kicktohandball)),
  Contestedtouncontested_diff=c(-diff(Contestedtouncontested),diff(Contestedtouncontested)),
  goal_conversion_diff=c(-diff(goal_conversion),diff(goal_conversion))
         )%>%
  filter(!margin==0)%>%
  filter(Season==2013)

Whats the next step now we have our dataset like the paper?

That would be to fit a logistic regression.

mylogit <- glm(win_loss ~ K_difference + M_difference + HB_difference+T_difference+I50_difference+CL_difference+CG_difference+CP_difference+UP_difference+CM_difference+goal_conversion+kicktohandball+Contestedtouncontested
               
               ,data = dataset_model, family ="binomial")
summary(mylogit)
## 
## Call:
## glm(formula = win_loss ~ K_difference + M_difference + HB_difference + 
##     T_difference + I50_difference + CL_difference + CG_difference + 
##     CP_difference + UP_difference + CM_difference + goal_conversion + 
##     kicktohandball + Contestedtouncontested, family = "binomial", 
##     data = dataset_model)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.17684  -0.42682  -0.00021   0.44963   2.48144  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -5.098957   1.632251  -3.124 0.001785 ** 
## K_difference            0.017970   0.026365   0.682 0.495513    
## M_difference           -0.040118   0.017819  -2.251 0.024356 *  
## HB_difference          -0.045039   0.029275  -1.538 0.123927    
## T_difference            0.027667   0.014805   1.869 0.061654 .  
## I50_difference          0.065159   0.018815   3.463 0.000534 ***
## CL_difference          -0.062603   0.021532  -2.907 0.003644 ** 
## CG_difference          -0.008463   0.020111  -0.421 0.673890    
## CP_difference           0.062228   0.028254   2.202 0.027633 *  
## UP_difference           0.054032   0.028425   1.901 0.057324 .  
## CM_difference           0.057289   0.041005   1.397 0.162379    
## goal_conversion         6.563385   1.600541   4.101 4.12e-05 ***
## kicktohandball          1.224616   0.900988   1.359 0.174086    
## Contestedtouncontested -0.771994   1.195178  -0.646 0.518328    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 546.20  on 393  degrees of freedom
## Residual deviance: 256.91  on 380  degrees of freedom
## AIC: 284.91
## 
## Number of Fisher Scoring iterations: 6

The question is why doesn’t our model here match the paper?

• I’m wrong (what should I do different thoughts feedback email me!)
• The data is different
• reading the paper again I didn’t remove draws whoops! (now fixed)

Some possible interesting changes to make