Brownlow Medal 2018 Predictions

About The Author

Professor Michael Bailey has worked as a biostatistician for 20 years and has published over 500 manuscripts in peer reviewed medical journals that have been cited more than 26,000 times. In 2005 he completed a PhD which outlined a statistical approach to predicting sporting outcomes and with a specific chapter dedicated to the Brownlow, he is the only known person to be awarded a PhD for the prediction of the Brownlow medal winner. He is back again this year with his Brownlow medal predictor 2018


Background

First awarded to Edward Greeves from Geelong in 1924, the Brownlow medal is the highest individual honour that can be bestowed upon an AFL footballer. Based on performance in each of the home and away matches played for the season, votes are assigned to the three best players (3 – first, 2 – second, 1- third) by the umpires that preside over the game. With suspended players deemed ineligible to win the medal, the Brownlow is awarded to the player perceived by umpires to be both the ‘best’ and ‘fairest’ for the season.


2018 Brownlow medal

Pure weight of numbers give Hawthorn midfielder Tom Mitchell the greatest probability of winning the 2018 Brownlow medal. In the 2017 season, Mitchell polled 25 votes to finish 3rd behind Dustin Martin and Patrick Dangerfield. In 2018, he has improved upon his 2017 season, remarkably gathering 40 or more possessions in a game on 11 separate occasions; a feat that has never previously been achieved in an AFL season.

Whilst a 40 possession game does not guarantee Brownlow votes, it does increase the probability significantly. Of the 113, 40+ games recorded over the past decade, only 11 (10%) of these games have been deemed not worthy of at least a vote by the umpires.  Prior to 2018, Mitchell had gathered 40+ possessions on 5 previous occasions for a total of 14 votes. With more votes awarded to players from the winning team, Hawthorn’s top 4 finish in 2018 will further serve to increase his probability of success.

Relative to his 2017 season, Dustin Martin’s performance in 2018 was markedly inferior. However, relative to the rest of the competition, he continued to excel. With a projected tally of 25 votes, Martin sits well clear in second place. Whilst Mitchell and Martin should finish first and second, the battle for 3rd place is up for grabs, with a further 12 players separated by only 3 votes.


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Patrick Dangerfield has polled at least 20 Brownlow votes in each of the past 6 seasons and the 2018 season should be no different. Conversely, burgeoning Melbourne midfielder Clayton Oliver has only previously polled 15 career Brownlow votes from 35 games prior to 2018. However, with 5 potential best on ground performances and a further 5 games in which he is ranked either 2nd or 3rd, Oliver should finish in the top 5.

One of the principal reasons for Oliver’s success in 2018 has been the stellar ruckwork of teammate Max Gawn. It is well established that ruckman have not performed well in Brownlow medal counting over the past decade, polling less than 5% of all votes.  Gawn will be seeking to challenge this stereotype and should finish in the top 10. Expect West Coast Eagles players Elliot Yeo and Andrew Gaff to also finish in the top 10. Despite missing the last 3 games and being ineligible to win due to suspension, Gaff should poll well, as should is teammate Yeo, who has had a breakout year with his 6 best ranked career games occurring in 2018.

 

NumberTEAMNAMEPredicted TotalWin %Place %Ranked 1Ranked 3Ranked 5
1HATom Mitchell290.660.93111616
2RIDustin Martin250.150.6241417
3GEPatrick Dangerfield200.040.2151617
4MEClayton Oliver200.040.2151014
5MEMax Gawn180.020.14712
6WEAndrew Gaff*180.020.151116
7WEElliot Yeo180.020.14911
8CAPatrick Cripps17<1%0.082913
9ADRory Laird17<1%0.0821013
10FOJack Macrae17<1%0.086813
11COBrody Grundy17<1%0.082912
12ESDyson Heppell17<1%0.085911
13FRNat Fyfe*17<1%0.08577
14SYJosh Kennedy17<1%0.0831014

*Ineligible through Suspension


Predicting the Brownlow medal winner

By using past information, it is possible to identify specific match and player features that are related to who will be awarded votes. For example, over the previous 5 years, 89% of all 3-votes, 77% of all 2-votes and 73% of all 1-votes have been awarded to players from the winning team, with the probabilities further increasing with the margin of victory. Similarly, the number of possession that each player gets is strongly related to the number of votes polled, with the leading possession winner for each match receiving votes in 62% of games over the past 5 years.

The natural ordering of the voting structure (3-2-1-0) lends itself to the use of a mathematical modelling process known as ordinal logistic regression. With a wealth of player information now readily available, by using information from over 2000 past matches, it is possible to identify and combine the effects of more than 20 contributing variables to produce a probability that each player will poll 3, 2 or 1 vote in each match.

The main contributing variables in the model include features of the game such as kicks, marks, handballs, goals and margin of victory, however features relating to the specific player such as quality of possession and past voting performance are also important contributors.  By combining these probabilities, each player is assigned a score from 0 to 3 for each match. By aggregating these scores over the entire season, it is then possible to assign each player with a predicted total of Brownlow votes for the season and with multiple simulation, a probability of success can then be obtained.


How well do the models perform?

Per game level

The model is remarkably good at identifying the leading players in each match, with the top 5 ranked players accounting for 95% of all 3-votes, 84% of all 2-votes and 74% of all 1-votes over the past 5 years. Furthermore, the leading ranked player in each match as determined by the modelling process has successfully poll 3 votes 61% of the time, 2 votes 22% of the time and 1 vote 9% of the time. When matches are further categorised according to whether there was 1 clear standout player (as opposed to 2 or 3 standout players) the leading ranked player will successful poll 3 votes in 67% of all games.

Per season level

When individual match scores are aggregated for the season, the modelling process can accurately predict the season tally of votes for 84% of all 657 players to within 1 vote. However, it is important to note that more than 70% of all footballers each season will not poll any votes at all. When considering the top 10 leading players each season, the modelling process will typically identify about 7 or 8 of the top 10 players and 3 or 4 of the top 5. When considering the winner, over the past 15 season, all winners have come from the top 4 ranked players each year, with the leading ranked player winning on 7 occasions, the second ranked player winning on 5 occasions and the third ranked player winning twice.


How the count will pan out on the night

With an AFL record 54 possessions in the first match of the season against Collingwood, expect Tom Mitchell to be an early leader. While Martin, Macrae and Fyfe may all lead at some stage during the first half of the season, Mitchell should be well clear by Round 15 and is unlikely to be headed from that point onwards.



Actual and predicted order and vote totals for the top 10 players for the past 5 years

YearPlayerFinishing OrderPredicted OrderActual votesPredicted Votes
2017D. Martin113635
P. Dangerfield223334
T. Mitchell332529
J. Kennedy452320
L. Franklin572218
J. Kelly662120
R. Sloane742021
M. Bontempelli881917
O. Wines9121815
D. Beams10261712
YearPlayerFinishing OrderPredicted OrderActual VotesPredicted Votes
2016P. Dangerfield113535
L. Parker2122617
D. Martin322523
R. Sloane452420
D. Hannebery532122
A. Gaff672118
A. Treloar7152116
M. Bontempelli882018
L. Neale9192015
R. Gray1091918
YearPlayerFinishing OrderPredicted OrderActual VotesPredicted Votes
2015N. Fyfe113128
M. Priddis232824
S. Mitchell322627
J. Kennedy442522
D. Hannebery552422
P. Dangerfield662221
D. Martin772120
D. Mundy8111916
C. Ward9261911
T. Goldstein1091820
YearPlayerFinishing OrderPredicted OrderActual OrderPredicted Order
2014M. Priddis142620
N. Fyfe212525
G. Ablett322223
L. Franklin4122215
J. Selwood532121
J. Kennedy662119
T. Boak792116
P. Dangerfield8172114
S. Johnson9131915
T. Cotchin1051820
YearPlayerFinishing OrderPredicted OrderActual OrderPredicted Order
2013G. Ablett112826
J. Sewlood232724
D. Swan322625
S. Johnson482519
P. Dangerfield5112217
S. Pendlebury662120
D. Hannebery7122117
T. Rockliff8152115
T.Cotchin991918
K. Jack10131917

By Team

Historically, when considering individual teams, the leading vote getter for each team was ranked first by the model 84% of the time, with the 2nd ranked player by the model getting the most votes 11% of the time.

Leading players for each club for the 2018 season.

Team Name Projected Ranked Ranked Ranked
Total 1st Top 3 Top 5
Adelaide R Laird 17 2 10 13
Adelaide M Crouch 12 2 5 10
Brisbane D Beams 15 3 5 8
Brisbane D Zorko 8 2 4 5
Carlton P Cripps 17 2 9 13
Carlton K Simpson 6 0 4 8
Collingwood B Grundy 17 2 9 12
Collingwood S Sidebottom 16 3 13 14
Collingwood S Pendlebury 15 3 8 12
Essendon D Heppell 17 5 9 11
Essendon Z Merrett 10 0 7 10
Western Bulldogs J Macrae 17 6 8 13
Western Bulldogs L Hunter 10 1 3 7
Western Bulldogs M Bontempelli 9 2 5 6
Fremantle N Fyfe 17 5 7 7
Fremantle L Neale 13 2 6 12
Geelong P Dangerfield 20 5 16 17
Geelong G Ablett 16 2 8 11
Geelong J Selwood 15 3 8 14
Gold Coast J Lyons 4 1 2 3
Gold Coast J Witts 4 0 2 5
Gold Coast A Young 4 2 2 2
GWS C Ward 14 2 7 12
GWS S Coniglio 13 1 7 12
GWS L Whitfield 12 2 4 11
Hawthorn T Mitchell 29 11 16 16
Hawthorn J Gunston 8 1 4 9
Hawthorn L Breust 8 1 5 8
Melbourne C Oliver 20 5 10 14
Melbourne M Gawn 18 4 7 12
North Melbourne S Higgins 15 4 6 11
North Melbourne B Cunnington 13 3 7 8
Port Adelaide O Wines 13 3 5 9
Port Adelaide R Gray 11 3 6 7
Richmond D Martin 25 4 14 17
Richmond T Cotchin 9 1 5 6
St. Kilda J Steven 11 2 5 7
St. Kilda S Ross 9 1 4 8
Sydney J Kennedy 17 3 10 14
Sydney L Franklin 13 3 6 12
West Coast A Gaff 18 5 11 16
West Coast E Yeo 18 4 9 11

Ranked 1st  =  Number of times each player was ranked as the leading player on the ground

Ranked Top 3 = Number of times each player was ranked in the top 3 players on the ground

Ranked Top 5 = Number of times each player was ranked in the top 5 players on the ground


Team Totals

By aggregating all individual player scores to a team level it is possible to derive a predicted vote tally for each team. The team to poll the most votes usually correlates with the team to win the most matches. However, in 2018, this is not the case. Whilst Richmond finished the home and away season at the top of the ladder, due to the evenness of their team with fewer standout individual performances, Richmond are unlikely to be the team to poll the most votes. Collingwood, Melbourne and Geelong are equally likely to poll the most team votes with projected totals of 90 votes.

Team polling the most votes in 2018

Rank Team Projected total Win %
1 Collingwood 90 27%
2 Melbourne 90 27%
3 Geelong 90 27%
4 Eagles 87 9%
5 Hawthorn 86 8%
6 Richmond 80 2%
7 Sydney 76 0%
8 GWS 73 0%
9 Adelaide 69 0%
10 Kangaroos 68 0%
11 Port 67 0%
12 Essendon 65 0%
13 Fremantle 53 0%
14 Bulldogs 53 0%
15 Brisbane 42 0%
16 StKilda 37 0%
17 Carlton 33 0%
18 Gold Coast 27 0%


Betting Strategy

 BACK – Tom Mitchell to Win for 10 Units

 BACK – (Market without Mitchell) Dustin Martin to Win for 4 Units


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