Beat the Geek: Actuarial tips on AFL and NRL this season

Read­ing time: 3 mins

Tay­lor Fry is cel­e­brat­ing the 10th year of its ‘Beat the Geek’ AFL and NRL tip­ping com­pe­ti­tion which pits tip­sters skills against the expert mod­el­ling pow­er of an actu­ar­i­al and ana­lyt­ics firm. Here, Gra­ham Tay­lor lets us in on com­mon tip­ster habits, what works and of course, who will win the 2017 pre­mier­ships!


How would you describe ‘Beat the Geek’ competition?

A Our “Beat the Geek” com­pe­ti­tion was con­ceived in 2008 by a for­mer col­league, James Sul­li­van, to engage and enter­tain our clients.  We ini­tial­ly devel­oped three mod­els (two GLMs and a Boost­ed deci­sion tree mod­el) to tip the win­ners of AFL games and invit­ed our clients to com­pete against these mod­els, with prizes for the best tip­sters to encour­age take-up.  We added a NRL com­pe­ti­tion in 2010 and this year is the 10th anniver­sary of our comps.

What have you learned about how people tip, what works and why?

There is a wide spec­trum of tip­sters (we are all just part of a dis­tri­b­u­tion).  Three com­mon approach­es include:

  • Slow and steady – wins the race – these peo­ple gen­er­al­ly tip favourites and often do well.
  • All or noth­ing – these tip­sters will take a few chances each round. They often win the week­ly prize (most win­ners picked cor­rect­ly in a round) although only very rarely win our comps – there’s typ­i­cal­ly a strong inverse cor­re­la­tion between week­ly round win­ners and comp win­ners.
  • Hit and hope – employ more of a ran­dom approach to tip­ping. At least they make every­one else look good.

Q Can you let us in on how the models work?

As already men­tioned we built two Gen­er­alised lin­ear mod­els (GLMs) and a Boost­ed deci­sion tree mod­el.  Due to the lack of data (and some lazi­ness in col­lect­ing addi­tion­al data) the GLMs are fair­ly sim­ple.  The tips can be viewed as ‘odds-plus’ mod­els, since bet­ting odds are by far the most use­ful pre­dic­tor –some of our tip­sters such as Michael Price (AMP) had already worked that out them­selves. The odds have tend­ed to car­ry small bias­es over time, so the addi­tion­al fac­tors we mod­el (even a sim­ple home ground fac­tor) can improve the sig­nal slight­ly. An inter­est­ing aspect of fit­ting the GLMs is that lim­it­ed amount of data requires sig­nif­i­cant care in the set­up choice of fac­tors. This is quite dif­fer­ent from some of our client mod­els where big datasets allow every­thing to be test­ed togeth­er. The boost­ed deci­sion tree mod­el builds a ‘black box’ mod­el, which is hard­er to inter­pret although it does pro­duce a rank of vari­able sig­nif­i­cance.

Q How have the models fared over the years?

A  I would give a score­card of 7 to 8 out of 10.  The mod­els have always fin­ished in the top half of our client com­pe­ti­tion, they have won the comps twice (one year fin­ish­ing 83rd out of 108,000 on the wider footytips.com.au  web­site). The NRL mod­els have tend­ed to fin­ish high­er on the leader­board than the AFL mod­els.  Over the years, the more man­u­al GLM mod­els have done mar­gin­al­ly bet­ter than the boost­ed deci­sion tree mod­el (hope­ful­ly my man­ag­er is read­ing this – there’s still some hope we don’t get replaced by robots).

Q Can money be made from modelling sports data?

A  Yes (David Walsh seems to have done OK).  A few years ago a deriv­a­tives trad­er and pro­fes­sion­al gam­bler approached us about sup­ply­ing him with cer­tain infor­ma­tion from the mod­els.  We had noticed that the mod­els were par­tic­u­lar good at pre­dict­ing a cer­tain aspect of a match and, on the lim­it­ed amount of data, reduced the book­ies’ typ­i­cal mar­gin from around 5% in their favour (that is, over the long run you get 95% back on a bet) to mod­er­ate­ly in our favour (slight­ly over 100%).  We did con­sid­er the busi­ness case – the size of the bets required, the rel­a­tive­ly small num­ber of AFL and NRL match­es and the non zero prob­a­bil­i­ty of ruin meant that we decid­ed against pur­su­ing it.  One relat­ed find­ing is that fixed odds bet­ting agen­cies do not always get it right.  A num­ber of years ago book­ies typ­i­cal­ly offered the same odds for a half-time/full-time draw in AFL and NRL irre­spec­tive of the head to head odds – this is clear­ly wrong, since well-matched teams are much more like­ly to tie. They have since amend­ed this, main­ly by reduc­ing odds on games expect­ed to be close.

 Hav­ing said all that almost every man and his dog has tried to beat the odds and the vast major­i­ty have failed.

Who should we back this AFL/NRL season (premiere predictions?) and why?

GWS in the AFL and Mel­bourne Storm in the NRL, although bor­ing­ly these are the same as the bet­ting mar­ket favourites.  If you are tak­ing part in a tip­ping comp this year then my rec­om­mend­ed strat­e­gy would be to tip with the book­ies’ favourites except for 4 or 5 times over the sea­son – if you get those cor­rect you will have a good chance of win­ning the comp!

Head­er Image by Scott Nolan.

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About the author

Graham Taylor

Graham has been at Taylor Fry since it formed in 1999. He uses statistical modelling to understand a diverse range of data, from New Zealand’s welfare system, to listed company financials that drive shareholder return, to predicting the outcomes of AFL and NRL matches!

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