The PremPredict package helps you to generate sensible predictions for individual games or an entire season of the Premier League.
You can find my automatically-updated Premier League predictor (that uses this codebase) on the landing page of its repo.
Installation
You can install the development version of PremPredict from GitHub with:
# install.packages("pak")
pak::pak("p0bs/PremPredict")Approach
I use a simplified version of David Firth’s approach and data from the Open Football repo on GitHub to predict the outcome of this season’s Premier League.
The predictions are based on a team’s strength, given its performance in recent times. But how should we define ‘recent’? In order to duck this question, you could choose a number of different time periods. Please note that 0.0% and 100.0% outcomes in the results do not necessarily signify certainty in their specific assessment, as:
- this model is typically used with more than 1,000 simulations; and more pertinently
- this model (like all models) is imperfect (but, I think, better than no model at all)
Example
Here is an example analysis, using data collected towards the end of the 2025/26 season.
First, we collect, combine and tidy the results data.
library(PremPredict)
data("example_thisSeason")
results_combined <- get_results(
results_thisSeason = example_thisSeason,
seasons = 1L
)
dim(results_combined)
#> [1] 760 9Note that we want to look back across this season (so far) and its predecessor.
game_latest <- calc_game_latest(results = results_combined)
results_filtered <- get_results_filtered(
results = results_combined,
index_game_latest = game_latest,
lookback_rounds = 76L
)
dplyr::glimpse(results_filtered)
#> Rows: 760
#> Columns: 8
#> $ matchday <date> 2024-08-16, 2024-08-17, 2024-08-17, 2024-08-17, 2024-08-17, …
#> $ homeTeam <chr> "MUN", "IPS", "ARS", "EVE", "NEW", "NOT", "WHU", "BRE", "CHE"…
#> $ awayTeam <chr> "FUL", "LIV", "WOL", "BRI", "SOU", "BOU", "AST", "CPA", "MCI"…
#> $ FTHG <dbl> 1, 0, 2, 0, 1, 1, 1, 2, 0, 1, 2, 0, 2, 4, 0, 4, 0, 1, 2, 2, 1…
#> $ FTAG <dbl> 0, 2, 0, 3, 0, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 0, 2, 1, 6, 0, 1…
#> $ FTR <chr> "H", "A", "H", "A", "H", "D", "A", "H", "A", "D", "H", "A", "…
#> $ played <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, T…
#> $ match <chr> "001", "002", "003", "004", "005", "006", "007", "008", "009"…For reference, we can see the prevailing table.
data_table_current <- example_thisSeason |>
calc_table_current()
data_table_current |>
print_table_current()| Team | Played | GD | Points |
|---|---|---|---|
| ARS | 8 | 12 | 19 |
| MCI | 8 | 11 | 16 |
| LIV | 8 | 3 | 15 |
| BOU | 8 | 3 | 15 |
| TOT | 8 | 7 | 14 |
| CHE | 8 | 7 | 14 |
| SUN | 8 | 3 | 14 |
| CPA | 8 | 4 | 13 |
| MUN | 8 | -1 | 13 |
| BRI | 8 | 1 | 12 |
| AST | 8 | 0 | 12 |
| EVE | 8 | 0 | 11 |
| BRE | 8 | -1 | 10 |
| NEW | 8 | 0 | 9 |
| FUL | 8 | -4 | 8 |
| LEE | 8 | -6 | 8 |
| BUR | 8 | -6 | 7 |
| NOT | 8 | -10 | 5 |
| WHU | 8 | -12 | 4 |
| WOL | 8 | -11 | 2 |
We can now model the strengths of the sides at home and away.
data_model <- results_filtered |>
model_prepare_frame() |>
model_run()
data_model
#>
#> Call:
#> gnm::gnm(formula = count ~ -1 + s + draw, eliminate = match,
#> family = stats::quasipoisson, data = modelframe, start = rep(0,
#> 2 * nTeams + 1))
#>
#> Coefficients of interest:
#> sARS_home sAST_home sBOU_home sBRE_home sBRI_home sBUR_home sCHE_home
#> 3.5887 3.3910 2.6672 2.7102 2.8616 3.0648 3.6256
#> sCPA_home sEVE_home sFUL_home sLEE_home sLIV_home sMCI_home sMUN_home
#> 2.1443 2.2261 2.2417 1.9925 4.2522 3.6907 2.2057
#> sNEW_home sNOT_home sSUN_home sTOT_home sWHU_home sWOL_home sARS_away
#> 3.3055 2.3953 4.3863 1.3325 1.0388 1.2459 3.5010
#> sAST_away sBOU_away sBRE_away sBRI_away sBUR_away sCHE_away sCPA_away
#> 2.2927 2.4675 2.0120 2.4678 -27.1656 2.5494 2.7080
#> sEVE_away sFUL_away sLEE_away sLIV_away sMCI_away sMUN_away sNEW_away
#> 1.9873 2.2584 1.1555 3.4702 2.7270 1.5472 2.3185
#> sNOT_away sSUN_away sTOT_away sWHU_away sWOL_away draw
#> 2.7723 1.7477 1.9700 2.1102 1.5372 0.5316
#>
#> Deviance: 849.1585
#> Pearson chi-squared: 904.8874
#> Residual df: 879[I will add further details and more explanation in due course.]
Next, we use these team strengths to model future games across the season.
data_parameters_unplayed <- data_model |>
model_extract_parameters()
data_model_parameters_unplayed <- model_parameters_unplayed(
results = results_filtered,
model_parameters = data_parameters_unplayed
)
data_points_expected_remaining <- data_model_parameters_unplayed |>
calc_points_expected_remaining()
calc_points_expected_total(
table_current = data_table_current,
points_expected = data_points_expected_remaining
) |>
knitr::kable()| midName | Exp_Points_Ave |
|---|---|
| Liverpool | 81.65226 |
| Arsenal | 81.16959 |
| Man City | 69.97195 |
| Chelsea | 66.99225 |
| Sunderland | 65.06298 |
| Aston Villa | 60.51185 |
| Newcastle | 59.42471 |
| Bournemouth | 58.81905 |
| Brighton | 58.03840 |
| Crystal Palace | 55.85868 |
| Brentford | 50.92066 |
| Notts Forest | 50.42615 |
| Everton | 48.28917 |
| Fulham | 47.81465 |
| Man Utd | 46.34688 |
| Tottenham | 40.73357 |
| Leeds Utd | 36.83454 |
| Burnley | 34.98184 |
| West Ham | 33.80618 |
| Wolves | 27.85355 |
On this basis, Liverpool look like slight favourites to win the season.
In order to project the likelihood of them becoming champions, however, we need to simulate many possible outcomes.
number_simulations <- 100000
data_simulate_games <- simulate_games(
data_model_parameters_unplayed = data_model_parameters_unplayed,
value_number_sims = number_simulations,
value_seed = 2602L
)
data_simulate_standings <- simulate_standings(
data_game_simulations = data_simulate_games,
data_table_latest = data_table_current
)
simulate_outcomes(
data_standings_simulations = data_simulate_standings,
value_number_sims = number_simulations
) |>
knitr::kable()| midName | champion | top_four | top_five | top_six | top_half | relegation |
|---|---|---|---|---|---|---|
| Arsenal | 0.46897 | 0.97800 | 0.99075 | 0.99599 | 0.99988 | 0.00000 |
| Liverpool | 0.46502 | 0.98261 | 0.99323 | 0.99723 | 0.99991 | 0.00000 |
| Man City | 0.04063 | 0.67953 | 0.80406 | 0.88171 | 0.98992 | 0.00001 |
| Chelsea | 0.01529 | 0.49399 | 0.66444 | 0.78457 | 0.97748 | 0.00002 |
| Sunderland | 0.00507 | 0.33786 | 0.53266 | 0.69126 | 0.96990 | 0.00000 |
| Aston Villa | 0.00173 | 0.14474 | 0.25864 | 0.39099 | 0.84651 | 0.00040 |
| Bournemouth | 0.00153 | 0.11152 | 0.20630 | 0.32196 | 0.78515 | 0.00061 |
| Brighton | 0.00070 | 0.08220 | 0.16333 | 0.26919 | 0.75086 | 0.00085 |
| Newcastle | 0.00068 | 0.10827 | 0.20384 | 0.32229 | 0.80497 | 0.00049 |
| Crystal Palace | 0.00031 | 0.05503 | 0.11214 | 0.19198 | 0.64739 | 0.00269 |
| Brentford | 0.00004 | 0.00874 | 0.02251 | 0.04878 | 0.33557 | 0.01433 |
| Notts Forest | 0.00001 | 0.00838 | 0.02179 | 0.04487 | 0.30181 | 0.02256 |
| Everton | 0.00001 | 0.00409 | 0.01133 | 0.02474 | 0.21777 | 0.03115 |
| Fulham | 0.00001 | 0.00323 | 0.00913 | 0.02041 | 0.18791 | 0.04094 |
| Man Utd | 0.00000 | 0.00167 | 0.00506 | 0.01200 | 0.14087 | 0.05987 |
| Tottenham | 0.00000 | 0.00013 | 0.00072 | 0.00181 | 0.03579 | 0.20681 |
| Leeds Utd | 0.00000 | 0.00000 | 0.00006 | 0.00018 | 0.00599 | 0.44530 |
| West Ham | 0.00000 | 0.00001 | 0.00001 | 0.00004 | 0.00210 | 0.67267 |
| Burnley | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00012 | 0.58946 |
| Wolves | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 0.00010 | 0.91184 |
Alternatively, this table can be generated, without calculating all intermediate steps, by running run_simulations.