https://github.com/ekstroem/euro2020
Predicting the Euro 2020 football winnders
https://github.com/ekstroem/euro2020
Last synced: 4 months ago
JSON representation
Predicting the Euro 2020 football winnders
- Host: GitHub
- URL: https://github.com/ekstroem/euro2020
- Owner: ekstroem
- Created: 2020-06-18T13:56:08.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-06-19T01:21:43.000Z (almost 6 years ago)
- Last Synced: 2025-10-10T15:36:03.015Z (8 months ago)
- Size: 3.91 KB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
# euro2020 (make that 2021)
Prediction competition for the Euro 2020 football winners from eRum 2020.
The slides from the talk can be [found here](https://biostatistics.dk/talks/eRum2020/pred.pdf).
## Competition rules
To participate in the competition you need to upload a $7 \times 24$
prediction matrix. The 24 columns represent the 24 teams and the 7
rows represent the 7 different ranks that can be determined from the
tournament: 1st, 2nd, 3rd, 4th, 5th-8th, 9th-16th, 17th-24th.
The prediction matrix should be uploaded here as a PULL REQUEST of an
R matrix object that can be read by `load()` on R v4.0.0 or higher.
The **order** of the columns (the 24 teams) will be provided here
after the final play-off match in November 2020. Then we know the
names of all the 24 teams.
The **elements** of the prediction matrix should be the probabilities
that the given country (column) will obtain a specific
rank. Consequently, all columns must sum to 1.
The rows of the prediction matrix must sum to `c(1, 1, 1, 1, 4, 8,
8)`, respectively. These numbers represent the number of teams that
can be part of each rank.
The **deadline** for submission i Sunday June 6th, 2021 at midnight CET.
## Determining the winner
We will use the Tournament Rank Probability Score (TRPS) to determine
the best tournament prediction.
The TRPS is defined in [this paper](https://arxiv.org/abs/1912.07364) and implemented in the `socceR` package which can be installed from CRAN:
```{r eval=FALSE}
install.packages("socceR")
```
Smaller numbers represents better predictions.
```{r}
library("socceR")
m1 <- matrix(c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, .5, .5, 0, 0, .5, .5), 4)
m1 # Prediction where certain on the top 2 ranks
trps(m1, c(1, 2, 3, 4))
```
## How to model the tournament results
You are free to model the tournament results in any way you want. You
can even conjure them up. Run a complicated random forest model. Or
make educated armchair guesses. All that counts is the 7 x 24 matrix.
## Summarizing
Hopefully we can summarize some of the approaches on eRum 2022. In any case we will have updates and the winner listed here on GitHub.