One of my favourite sites is squiggle, mainly because I like to check out what other people have tipped, what their respective margins are and how they are aligned.

As some of you know James and I have been working on an AFL R package called fitzRoy

One of our main goals is to get more people to build out AFL models.

For me that’s mainly because I like reading AFL stats content, be it probably a bit too much!

Having done a presentation at useR, one of the main problems I found with getting people wanting to give it a go is that they feel as though building their own model is simply too complex, that they are out of their depth.

Well I am here to say, that’s not true. Anyone can give it a go.

I think there are many camps to the AFL stats fanbase. One of which I would like to try and include more.

That is those who feel as though they can’t contribute or can’t analyse simply because they don’t know R/How to build out models.

So while you might be reading other blogs and sure they are “coding” it up from “scratch”. I think a missing point to this thought is while that’s true, it also doesn’t have to be you. Lots of people develop packages and answer questions on sites like stackoverflow.

So if you have ever wanted to build your own ELO for AFL why not use this ELO package. That’s what it was designed for, for people to use it.

library(tidyverse)

## ── Attaching packages ───────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.0     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   0.8.2
## ✔ tidyr   0.8.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## ── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(elo)
library(fitzRoy)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

results <- fitzRoy::get_match_results()
fixture <- fitzRoy::get_fixture(2019)

head(results)

## # A tibble: 6 x 16
##    Game Date       Round Home.Team Home.Goals Home.Behinds Home.Points
##   <dbl> <date>     <chr> <chr>          <int>        <int>       <int>
## 1     1 1897-05-08 R1    Fitzroy            6           13          49
## 2     2 1897-05-08 R1    Collingw…          5           11          41
## 3     3 1897-05-08 R1    Geelong            3            6          24
## 4     4 1897-05-08 R1    Sydney             3            9          27
## 5     5 1897-05-15 R2    Sydney             6            4          40
## 6     6 1897-05-15 R2    Essendon           4            6          30
## # … with 9 more variables: Away.Team <chr>, Away.Goals <int>,
## #   Away.Behinds <int>, Away.Points <int>, Venue <chr>, Margin <int>,
## #   Season <dbl>, Round.Type <chr>, Round.Number <int>

results <- results %>%
  mutate(seas_rnd = paste0(Season, ".", Round.Number),
         First.Game = ifelse(Round.Number == 1, TRUE, FALSE))

head(results)

## # A tibble: 6 x 18
##    Game Date       Round Home.Team Home.Goals Home.Behinds Home.Points
##   <dbl> <date>     <chr> <chr>          <int>        <int>       <int>
## 1     1 1897-05-08 R1    Fitzroy            6           13          49
## 2     2 1897-05-08 R1    Collingw…          5           11          41
## 3     3 1897-05-08 R1    Geelong            3            6          24
## 4     4 1897-05-08 R1    Sydney             3            9          27
## 5     5 1897-05-15 R2    Sydney             6            4          40
## 6     6 1897-05-15 R2    Essendon           4            6          30
## # … with 11 more variables: Away.Team <chr>, Away.Goals <int>,
## #   Away.Behinds <int>, Away.Points <int>, Venue <chr>, Margin <int>,
## #   Season <dbl>, Round.Type <chr>, Round.Number <int>, seas_rnd <chr>,
## #   First.Game <lgl>

fixture <- fixture %>%
  filter(Date > max(results$Date)) %>%
  mutate(Date = ymd(format(Date, "%Y-%m-%d"))) %>%
  rename(Round.Number = Round)

# Set parameters
HGA <- 30 # home ground advantage
carryOver <- 0.5 # season carry over
k_val <- 20 # update weighting factor


map_margin_to_outcome <- function(margin, marg.max = 80, marg.min = -80){
  norm <- (margin - marg.min)/(marg.max - marg.min)
  norm %>% pmin(1) %>% pmax(0)
}

# Run ELO
elo.data <- elo.run(
  map_margin_to_outcome(Home.Points - Away.Points) ~
  adjust(Home.Team, HGA) +
    Away.Team +
    group(seas_rnd) +
    regress(First.Game, 1500, carryOver),
  k = k_val,
  data = results
)

as.data.frame(elo.data) %>% tail()

##               team.A          team.B       p.A  wins.A    update    elo.A
## 15528         Sydney      Gold Coast 0.5849803 0.76250  3.550395 1497.135
## 15529    Collingwood North Melbourne 0.5848363 0.22500 -7.196726 1518.862
## 15530  Port Adelaide       Footscray 0.5746598 0.34375 -4.618196 1502.893
## 15531       St Kilda        Richmond 0.5271466 0.29375 -4.667931 1471.617
## 15532 Brisbane Lions       Melbourne 0.5833187 0.70625  2.458626 1508.817
## 15533      Fremantle         Carlton 0.5790813 0.47500 -2.081626 1502.480
##          elo.B
## 15528 1460.405
## 15529 1503.730
## 15530 1489.859
## 15531 1492.071
## 15532 1475.459
## 15533 1481.227

as.matrix(elo.data) %>% tail()

##         Adelaide Brisbane Lions  Carlton Collingwood Essendon Fitzroy
## [2791,] 1513.852       1499.096 1482.224    1524.114 1504.841    1500
## [2792,] 1513.972       1500.577 1478.602    1522.139 1508.463    1500
## [2793,] 1516.079       1498.933 1480.246    1525.196 1508.463    1500
## [2794,] 1518.601       1498.933 1479.145    1525.196 1509.635    1500
## [2795,] 1518.601       1506.358 1479.145    1526.059 1506.024    1500
## [2796,] 1516.706       1508.817 1481.227    1518.862 1506.512    1500
##         Footscray Fremantle  Geelong Gold Coast      GWS Hawthorn
## [2791,]  1491.370  1502.481 1533.124   1476.197 1517.994 1497.941
## [2792,]  1485.004  1504.456 1533.719   1468.234 1525.957 1496.460
## [2793,]  1485.004  1504.456 1541.934   1464.799 1523.850 1496.460
## [2794,]  1486.105  1506.316 1541.934   1463.956 1526.889 1495.288
## [2795,]  1485.241  1504.562 1540.387   1463.956 1526.889 1493.680
## [2796,]  1489.859  1502.480 1542.282   1460.405 1526.402 1492.532
##         Melbourne North Melbourne Port Adelaide Richmond St Kilda   Sydney
## [2791,]  1479.339        1492.084      1498.387 1502.193 1492.304 1487.083
## [2792,]  1479.219        1496.137      1507.824 1498.140 1482.867 1486.488
## [2793,]  1476.163        1499.573      1507.824 1489.926 1482.867 1491.976
## [2794,]  1476.163        1496.533      1505.964 1487.403 1483.710 1491.976
## [2795,]  1477.917        1496.533      1507.511 1487.403 1476.285 1493.585
## [2796,]  1475.459        1503.730      1502.893 1492.071 1471.617 1497.135
##         University West Coast
## [2791,]       1500   1505.376
## [2792,]       1500   1511.741
## [2793,]       1500   1506.252
## [2794,]       1500   1506.252
## [2795,]       1500   1509.863
## [2796,]       1500   1511.010

final.elos(elo.data)

##        Adelaide  Brisbane Lions         Carlton     Collingwood 
##        1516.706        1508.817        1481.227        1518.862 
##        Essendon         Fitzroy       Footscray       Fremantle 
##        1506.512        1380.902        1489.859        1502.480 
##         Geelong      Gold Coast             GWS        Hawthorn 
##        1542.282        1460.405        1526.402        1492.532 
##       Melbourne North Melbourne   Port Adelaide        Richmond 
##        1475.459        1503.730        1502.893        1492.071 
##        St Kilda          Sydney      University      West Coast 
##        1471.617        1497.135        1412.936        1511.010

fixture <- fixture %>%
  mutate(Prob = predict(elo.data, newdata = fixture))

head(fixture)

## # A tibble: 6 x 8
##   Date       Season Season.Game Round.Number Home.Team Away.Team Venue
##   <date>      <int>       <int>        <dbl> <chr>     <chr>     <chr>
## 1 2019-06-30   2019           1           15 St Kilda  Richmond  Marv…
## 2 2019-06-30   2019           1           15 Brisbane… Melbourne Gabba
## 3 2019-06-30   2019           1           15 Fremantle Carlton   Optu…
## 4 2019-07-05   2019           1           16 Hawthorn  Collingw… MCG  
## 5 2019-07-06   2019           1           16 Essendon  Sydney    MCG  
## 6 2019-07-06   2019           1           16 Gold Coa… Richmond  Metr…
## # … with 1 more variable: Prob <dbl>

So if you run that script end to end you should have a working ELO model that you can play around with.