I got only one of the two final games right. Well done Springboks! All in all I got 34 games right out of 48, a global success rate of 70.8%. That was really fun.
Results of final games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| ARG | ENG | ENG | 1 |
| NZL | RSA | RSA | 0 |
Future works: refactor code, do backtesting to figure out better time split, rewrite modeling code to include home advantage, try this out on football competitions.
Plots for the two final games:
Slight advantages are visible in the boxplots, but we'll see what happens!
The semi final games were both predictable! All in all I got 33 games right out of 46, a global success rate of 71.7%.
Results of semi final games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| ARG | NZL | NZL | 1 |
| ENG | RSA | RSA | 1 |
Plots for semi finals bets:
Winners should be obvious... at least in the boxplots!
The quarter final games were a total miss for me, I got zero game right! All in all I still got 31 games right out of 44, a global success rate of 70.5%.
Results of quarter final games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| WAL | ARG | ARG | 0 |
| IRE | NZL | NZL | 0 |
| ENG | FIJ | ENG | 0 |
| FRA | RSA | RSA | 0 |
Plots for quarter finals bets:
There could be a surprise in... well at this point, anything goes!
So, how did it go with the fifth round? Well I got 75% of winners right (6 out 8). The surprise (POR) did not come from where I thought (ROU). All in all I got 31 games right out of 40, so 77.5% success rate for the pool games! Yay for me.
Results of round 5 of pool games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| NZL | URU | NZL | 1 |
| FRA | ITA | FRA | 1 |
| WAL | GEO | WAL | 1 |
| ENG | SAM | ENG | 1 |
| IRE | SCO | IRE | 1 |
| JPN | ARG | ARG | 1 |
| TGA | ROU | TGA | 0 |
| FIJ | POR | POR | 0 |
Plots for pool #5 games bets:
There could be a surprise in WAL-GEO, ENG-SAM and TGA-ROU.
New: Models are built used time periods that make some sense, from 2020 to today (roughly, 6 months intervals). Also, refactored.
Future work: Do backtesting. Use other modeling possibilities (in choix, or find other implementation).
So, how did it go with the fourth round? Well I got 87.5% of winners right (7 out 8). All in all I got 25 games right out of 32, so 78% success rate so far.
Results of round 4 of pool games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| URU | NAM | URU | 1 |
| JAP | SAM | JAP | 0 |
| NZL | ITA | NZL | 1 |
| ARG | CHI | ARG | 1 |
| FIJ | GEO | FIJ | 1 |
| SCO | ROM | SCO | 1 |
| AUS | POR | AUS | 1 |
| RSA | TGA | RSA | 1 |
In the last few days I managed to hack my way to where I wanted to go:
- looking back in time, use an increasing quantity of data to fit several Bradley-Terry models
- use those models to generate several win probabilities for the same event
- make boxplots!
Here is a beautiful plot for pool #4 bets:
Note to self: 3 numbers are not enough to make a boxplot! Also, using one-year increments to go back in time makes no particular sense.
I used some excellent code from octonion to figure out the syntax of the API, and could not resist looking at his own predictions:
- parsing code: https://github.com/octonion/rugby/blob/master/world_rugby/scrapers/matches.rb
- prediction code: https://github.com/octonion/rugby/blob/master/world_rugby/men/lmer.R
- predictions he made: https://github.com/octonion/rugby/blob/master/world_rugby/men/predict.txt
(Also, refactored the python files.)
For this new round of pool games, we see that the bets are sometimes different when using rankings or B-T.
Pool #4 games
** FROM RATINGS **
URU 99.1% -- NAM 0.8%
JPN 23.2% -- SAM 76.7%
NZL 99.9% -- ITA 0.0%
ARG 99.9% -- CHI 0.0%
FIJ 99.9% -- GEO 0.0%
SCO 99.9% -- ROU 0.0%
AUS 99.8% -- POR 0.1%
RSA 99.9% -- TGA 0.0%
** FROM BRADLEY-TERRY **
URU 43.2% -- NAM 56.7%
JPN 61.9% -- SAM 38.0%
NZL 91.7% -- ITA 8.2%
ARG 83.6% -- CHI 16.3%
FIJ 49.5% -- GEO 50.4%
SCO 59.2% -- ROU 40.7%
AUS 80.7% -- POR 19.2%
RSA 83.7% -- TGA 16.2%
Notes to self: There are many algorithms in choix. URU-NAM still seems off in BT. Also need a way to take draws into account.
So, how did it go with the third round? Well I got 62.5% of winners right (5 out 8). All in all I got 18 out of 24, so 75% right so far.
Results of round 3 of pool games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| ITA | URU | ITA | 1 |
| FRA | NAM | FRA | 1 |
| ARG | SAM | ARG | 1 |
| GEO | POR | POR | 0 |
| ENG | CHI | ENG | 1 |
| RSA | IRE | IRE | 0 |
| SCO | TGA | SCO | 1 |
| WAL | AUS | WAL | 0 |
I've been doing it wrong since day one. The ranking score given by World Rugby are not good input to choix lib, as they were not computed by Bradley-Terry.
Those 99.9% looked too good to be true, of course. So: I downloaded past games scores (2013 onwards, maybe it's too much), computed the rankings properly , and computed probabilities properly. Also, refactoring of the code. Here are new numbers that make more sense.
Pool #3 games - sensible!
ITA 49.8% -- URU 50.1%
FRA 74.9% -- NAM 25.0%
ARG 62.5% -- SAM 37.4%
GEO 72.7% -- POR 27.2%
ENG 93.1% -- CHI 6.8%
RSA 50.5% -- IRE 49.4%
SCO 71.2% -- TGA 28.7%
WAL 45.7% -- AUS 54.2%
Notes to self: I still need some sort of confidence interval. Also some data cleaning would be nice, maybe use only past matches from teams that actually compete in the world cup - the bets on ITA-URU seem off, maybe that's due to bad data. See scrape-matches.py for the calls to a nice API that has loads of past data.
Here they are, done with the latest ratings. Three games look undecided.
Pool #3 games!
ITA 99.9% -- URU 0.0%
FRA 99.9% -- NAM 0.0%
ARG 80.1% -- SAM 19.8%
GEO 99.6% -- POR 0.3%
ENG 99.9% -- CHI 0.0%
RSA 46.1% -- IRE 53.8%
SCO 99.9% -- TGA 0.0%
WAL 84.0% -- AUS 15.9%
So, how did it go with the second phase? Well I got 87.5% of winners right (7 out 8). Admittedly this round 2 was not surprising except for FIJ which seems to be a real outlier performer! All in all I got 13 out of 16, so 81.25% right so far.
Results of second round of pool games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| FRA | URU | FRA | 1 |
| NZL | NAM | NZL | 1 |
| SAM | CHI | SAM | 1 |
| WAL | POR | WAL | 1 |
| IRE | TGA | IRE | 1 |
| RSA | ROU | RSA | 1 |
| AUS | FIJ | FIJ | 0 |
| ENG | JPN | ENG | 1 |
Here they are, done with the latest ratings. Seems all straightforward, but who knows what will happen?
Pool #2 games!
FRA 99.9% -- URU 0.0%
NZL 99.9% -- NAM 0.0%
SAM 99.9% -- CHI 0.0%
WAL 99.9% -- POR 0.0%
IRE 99.9% -- TGA 0.0%
RSA 99.9% -- ROU 0.0%
AUS 98.0% -- FIJ 1.9%
ENG 99.9% -- JPN 0.0%
Notes to self: What I would like to do - get some sort of confidence interval. Maybe use historical data to compute my own ranking, using Bradley-Terry, which is what I wanted to do initially. By using more or less historical data, I could make several models, and get a confidence interval like that.
So, how did it go with the first phase? Well I got 75% of winners right (6 out 8)
Results of first round of pool games
My bets are in bold.
| Team 1 | Team 2 | Winner | 1 if the bet is right |
|---|---|---|---|
| FRA | NZL | FRA | 1 |
| ITA | NAM | ITA | 1 |
| IRE | ROU | IRE | 1 |
| AUS | GEO | AUS | 1 |
| ENG | ARG | ENG | 0 |
| RSA | SCO | RSA | 1 |
| WAL | FIJ | WAL | 0 |
| JPN | CHI | JPN | 1 |
OK I'm super late since it starts tomorrow!
Links I found useful:
- Official rankings, but hard to use as is: https://www.world.rugby/tournaments/rankings/mru
- API to get the rankings in json form: https://api.wr-rims-prod.pulselive.com/rugby/v3/rankings/mru
- Sourced from https://github.com/rawling/wr-calc/ and https://rawling.github.io/wr-calc/
Got the World Rankings and points in json, parsed them, then used choix to generate probabilities of win for all possible games. I used this tutorial: https://github.com/lucasmaystre/choix/blob/master/notebooks/intro-pairwise.ipynb
Then, simply print probabilities as % for the pool games. GPT sped me up a lot, I used it as a coding assistant.
Next, I would like to see whether the points ranking from the Rugby world site is really a good input for choix, and get some kind of confidence interval. Additionally I could generate my own parameters based on past games, that would be more in the Bradley-Terry spirit.
By the way, here are the predictions:
Pool games!
FRA 54.1% -- NZL 45.8%
ITA 99.9% -- NAM 0.0%
IRE 99.9% -- ROU 0.0%
AUS 97.4% -- GEO 2.5%
ENG 28.6% -- ARG 71.3%
RSA 99.9% -- SCO 0.0%
WAL 11.6% -- FIJ 88.3%
JPN 99.9% -- CHI 0.0%
FRA 99.9% -- URU 0.0%
NZL 99.9% -- NAM 0.0%
IRE 99.9% -- TGA 0.0%
WAL 99.9% -- POR 0.0%
SAM 99.9% -- CHI 0.0%
RSA 99.9% -- ROU 0.0%
AUS 39.8% -- FIJ 60.1%
ENG 99.8% -- JPN 0.1%
ITA 99.9% -- URU 0.0%
FRA 99.9% -- NAM 0.0%
ARG 99.0% -- SAM 0.9%
RSA 32.2% -- IRE 67.7%
GEO 99.9% -- POR 0.0%
ENG 99.9% -- CHI 0.0%
SCO 99.9% -- TGA 0.0%
WAL 16.6% -- AUS 83.3%
URU 99.3% -- NAM 0.6%
JPN 5.2% -- SAM 94.7%
NZL 99.9% -- ITA 0.0%
SCO 99.9% -- ROU 0.0%
FIJ 98.2% -- GEO 1.7%
ARG 99.9% -- CHI 0.0%
AUS 99.9% -- POR 0.0%
NZL 99.9% -- URU 0.0%
FRA 99.9% -- ITA 0.0%
IRE 99.9% -- SCO 0.0%
WAL 88.3% -- GEO 11.6%
ENG 97.7% -- SAM 2.2%
TGA 99.6% -- ROU 0.3%
FIJ 99.9% -- POR 0.0%
JPN 0.0% -- ARG 99.9%
The Rugby World cup is about to start, and I have been invited to a betting league by my colleagues. I have been using random Excel formulas for years to predict outcomes in the Football world cups, with limited success. This time I want to explore how to generate sensible bets in a data-driven fashion. This is my journal. Yay!
So, first thing, I remembered my ex-colleague Dr. Fabien Llobell explained how to predict winners for the football world cup in 2022. He is head of research at the company that sells a widely used statistics plugin for Excel (disclaimer: I used to work there too), so for sure he knows what he's talking about. Here is his write-up: https://medium.com/xlstat/football-score-predictions-with-the-bradley-terry-model-e0a8a7ebfd83
So, the take-away is: use the Bradley-Terry model. Fine. I want to understand what this model does, how it works. I want to generate a probability of winning the next game for arbitrary teams, with confidence level. And I want to do it in Python.
Here are a few pointers that seem of interest.
- Mandatory Wikipedia link: https://en.wikipedia.org/wiki/Bradley%E2%80%93Terry_model
- Of course R has a sophisticated implementation. Reading this page will help: https://www.r-bloggers.com/2022/02/what-is-the-bradley-terry-model/
- This guy has Jupyter notebooks that seem to do just what I want. This should be useful: https://github.com/sezenack/Bradley-Terry-Sports-Model
choixis a Python lib that has a Bradley-Terry implementation, with tutorials. High hopes for this: https://github.com/lucasmaystre/choix
OK that should be good starting points. Of course, I also want to use GPT to help me out. Let's see how this goes.