Setting up this thread for That Guy, angelo, rohan and anyone else who wants to brainstorm approaches for modelling NRL games. Aim is to try to see how one might use stats combined with situational analysis to pick accurately ATS at a rate of 55%+.
I'll start by giving my current thoughts.
1) You need at least 5 games worth of stats before you even think about using a model accurately. You also have to tread carefully the week before and after state of origin, and in the last games of the season when teams have nothing top play for. That leaves you at most only 18 weeks or so of "normal" betting conditions to use a modelling approach.
2) NRL is a small market, and heavily driven by "squares" who go off what the commentators on Ch 9 and FOXTEL say and what they read in the Daily Telegraph - these means there is a much higher probability of using modelling to determine an inefficiency in the lines being set than sports like NFL, MLB and NBA. The key is to not "become" a square, and stats can help you do that by not over-emphasising last weeks game or plays that stood out, games you watched closely vs over 10 beers at the local, etc
3) Focus should be on only the absolute, most important stats. Here is my current list, interested to see what others have here.
Offensive efficiency
Metres gained/set
Points gained/set
Line breaks/set
Defensive efficiency
Metres allowed/set
Points allowed/set
Line breaks allowed/set
Error rate
Errors/set
Missed tackles/set
Others? Kicking game?
My thinking is to work out these numbers for all teams, assign 1 to the lowest number, 10 to the highest and allocate numbers to other teams depending on where they fit along the range - i.e. let's take points gained/set, let's say the best team gets 1 point/set, the worst gets 0.5 point/set. Range is 0.5, divide by 10, so each increment is 0.05. A team then that gets 0.8 points/set would be ranked a 6.
All these stats should be able to be broken down by:
Home vs Away
Top 8 vs non-Top 8
And once you have (say) 10 games you might arguably also eliminate any outliers - the games on either end of the extreme for a given team.
4) Along with these stats you assign each team a "Home" ranking and an "Away" ranking depending on the strength of their home ground advantage, and their relative away vs home performance.
Home ranking can be done by looking up historical stats, going back no more than 2-3 seasons, and allocating it a number from 1-10. I haven't done it yet, but I expect NQ, NZ, Melbourne, Brookvale to be the top home ground advantages.
5) Along with the stats above you want a sense of a teams momentum, to do that I'd again calculate a number out of 10 to factor in how they performed in the last "X" games, I think I'd start with "X" being 5.
So you'd use:
Wins in last 5 games
Key stat improvement in last 5 games vs season average
Is a team getting better and better? Regressing? You want to somehow capture this.
6) Once you have all these numbers out of 10 you then just need to determine how then it calculates what you think the "true" game spread should be
This requires a bit of trial and error and - ideally - back-testing and regression analysis. This is really the "magic" so I expect it to take the most time.
Even after all this you still need to factor in situational elements - motivation, weather, etc - and then of course injuries. The ideal world is where you have a model that can very quickly show you were there is potential line value and then allow you to focus your time accordingly.
These are just initial thoughts - interested to hear other points of view on them, devil's advocate, etc.
If I get time I'm going to run through the methodology above with a given game given the stats from this season so far and see how it might be applied.
I'll start by giving my current thoughts.
1) You need at least 5 games worth of stats before you even think about using a model accurately. You also have to tread carefully the week before and after state of origin, and in the last games of the season when teams have nothing top play for. That leaves you at most only 18 weeks or so of "normal" betting conditions to use a modelling approach.
2) NRL is a small market, and heavily driven by "squares" who go off what the commentators on Ch 9 and FOXTEL say and what they read in the Daily Telegraph - these means there is a much higher probability of using modelling to determine an inefficiency in the lines being set than sports like NFL, MLB and NBA. The key is to not "become" a square, and stats can help you do that by not over-emphasising last weeks game or plays that stood out, games you watched closely vs over 10 beers at the local, etc
3) Focus should be on only the absolute, most important stats. Here is my current list, interested to see what others have here.
Offensive efficiency
Metres gained/set
Points gained/set
Line breaks/set
Defensive efficiency
Metres allowed/set
Points allowed/set
Line breaks allowed/set
Error rate
Errors/set
Missed tackles/set
Others? Kicking game?
My thinking is to work out these numbers for all teams, assign 1 to the lowest number, 10 to the highest and allocate numbers to other teams depending on where they fit along the range - i.e. let's take points gained/set, let's say the best team gets 1 point/set, the worst gets 0.5 point/set. Range is 0.5, divide by 10, so each increment is 0.05. A team then that gets 0.8 points/set would be ranked a 6.
All these stats should be able to be broken down by:
Home vs Away
Top 8 vs non-Top 8
And once you have (say) 10 games you might arguably also eliminate any outliers - the games on either end of the extreme for a given team.
4) Along with these stats you assign each team a "Home" ranking and an "Away" ranking depending on the strength of their home ground advantage, and their relative away vs home performance.
Home ranking can be done by looking up historical stats, going back no more than 2-3 seasons, and allocating it a number from 1-10. I haven't done it yet, but I expect NQ, NZ, Melbourne, Brookvale to be the top home ground advantages.
5) Along with the stats above you want a sense of a teams momentum, to do that I'd again calculate a number out of 10 to factor in how they performed in the last "X" games, I think I'd start with "X" being 5.
So you'd use:
Wins in last 5 games
Key stat improvement in last 5 games vs season average
Is a team getting better and better? Regressing? You want to somehow capture this.
6) Once you have all these numbers out of 10 you then just need to determine how then it calculates what you think the "true" game spread should be
This requires a bit of trial and error and - ideally - back-testing and regression analysis. This is really the "magic" so I expect it to take the most time.Even after all this you still need to factor in situational elements - motivation, weather, etc - and then of course injuries. The ideal world is where you have a model that can very quickly show you were there is potential line value and then allow you to focus your time accordingly.
These are just initial thoughts - interested to hear other points of view on them, devil's advocate, etc.
If I get time I'm going to run through the methodology above with a given game given the stats from this season so far and see how it might be applied.
