Association Rules in Football: What Leads to a Home Win?
Konrad Danel
2026-01-29
library(dplyr)
library(tidyr)
library(readr)
library(stringr)
library(arules)
library(arulesViz)
library(ggplot2)Introduction
This project applies association rule mining to English Premier League match data. The goal is to discover interpretable patterns in match statistics that are strongly associated with a home win. We treat each match as a “transaction” and convert numerical match features (e.g., shots, corners, cards) into categorical items (high/low/advantage). Rules are evaluated using support, confidence, and lift.
Data
The dataset comes from football-data.co.uk (Premier League) and is provided as a CSV file. We use basic full-time match statistics and the full-time result. We keep the following columns:
FTR (full time result: H/D/A)
FTHG, FTAG (goals)
HS, AS (shots)
HC, AC (corners)
HY, AY (yellow cards)
df <- read_csv("E0.csv", show_col_types = FALSE)
glimpse(df)## Rows: 380
## Columns: 120
## $ Div <chr> "E0", "E0", "E0", "E0", "E0", "E0", "E0", "E0", "E0", "E0"…
## $ Date <chr> "16/08/2024", "17/08/2024", "17/08/2024", "17/08/2024", "1…
## $ Time <time> 20:00:00, 12:30:00, 15:00:00, 15:00:00, 15:00:00, 15:00:0…
## $ HomeTeam <chr> "Man United", "Ipswich", "Arsenal", "Everton", "Newcastle"…
## $ AwayTeam <chr> "Fulham", "Liverpool", "Wolves", "Brighton", "Southampton"…
## $ FTHG <dbl> 1, 0, 2, 0, 1, 1, 1, 2, 0, 1, 2, 0, 2, 4, 0, 4, 0, 1, 2, 2…
## $ FTAG <dbl> 0, 2, 0, 3, 0, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 0, 2, 1, 6, 0…
## $ FTR <chr> "H", "A", "H", "A", "H", "D", "A", "H", "A", "D", "H", "A"…
## $ HTHG <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 3, 0, 2, 0, 1, 2, 1…
## $ HTAG <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 2, 0…
## $ HTR <chr> "D", "D", "H", "A", "H", "H", "D", "H", "A", "A", "H", "D"…
## $ Referee <chr> "R Jones", "T Robinson", "J Gillett", "S Hooper", "C Pawso…
## $ HS <dbl> 14, 7, 18, 9, 3, 14, 14, 9, 10, 7, 14, 14, 18, 14, 5, 13, …
## $ AS <dbl> 10, 18, 9, 10, 19, 13, 15, 14, 11, 15, 11, 18, 10, 1, 23, …
## $ HST <dbl> 5, 2, 6, 1, 1, 8, 3, 5, 3, 3, 5, 2, 6, 5, 1, 7, 3, 4, 4, 8…
## $ AST <dbl> 2, 5, 3, 5, 4, 4, 3, 6, 5, 7, 4, 3, 4, 1, 8, 1, 4, 5, 8, 2…
## $ HF <dbl> 12, 9, 17, 8, 15, 17, 18, 6, 12, 11, 9, 9, 14, 4, 14, 11, …
## $ AF <dbl> 10, 18, 14, 8, 16, 8, 11, 15, 9, 12, 13, 17, 13, 15, 14, 1…
## $ HC <dbl> 7, 2, 8, 1, 3, 2, 5, 4, 4, 2, 4, 3, 7, 10, 4, 12, 4, 8, 5,…
## $ AC <dbl> 8, 10, 2, 5, 12, 6, 3, 7, 3, 13, 4, 3, 5, 1, 10, 5, 1, 9, …
## $ HY <dbl> 2, 3, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 0, 1, 2, 2, 2…
## $ AY <dbl> 3, 1, 2, 1, 4, 3, 2, 5, 1, 1, 2, 1, 2, 3, 3, 0, 3, 2, 3, 3…
## $ HR <dbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ AR <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ B365H <dbl> 1.60, 8.50, 1.18, 2.63, 1.36, 2.45, 2.45, 2.40, 4.10, 5.00…
## $ B365D <dbl> 4.20, 5.50, 7.50, 3.30, 5.25, 3.50, 3.60, 3.40, 3.90, 4.33…
## $ B365A <dbl> 5.25, 1.33, 13.00, 2.63, 8.00, 2.80, 2.75, 2.90, 1.80, 1.6…
## $ BWH <dbl> 1.60, 7.50, 1.20, 2.65, 1.35, 2.45, 2.45, 2.40, 3.90, 5.00…
## $ BWD <dbl> 4.40, 5.50, 7.50, 3.40, 5.50, 3.50, 3.60, 3.40, 4.10, 4.33…
## $ BWA <dbl> 5.25, 1.36, 13.50, 2.60, 7.75, 2.80, 2.70, 2.87, 1.82, 1.6…
## $ BFH <dbl> 1.60, 8.50, 1.15, 2.70, 1.33, 2.45, 2.40, 2.45, 4.10, 5.00…
## $ BFD <dbl> 4.33, 5.50, 8.00, 3.40, 5.50, 3.50, 3.60, 3.40, 3.90, 4.33…
## $ BFA <dbl> 5.00, 1.33, 16.00, 2.63, 8.50, 2.88, 2.80, 2.88, 1.80, 1.5…
## $ PSH <dbl> 1.63, 8.18, 1.16, 2.73, 1.35, 2.47, 2.49, 2.50, 4.19, 5.09…
## $ PSD <dbl> 4.38, 5.84, 8.56, 3.36, 5.70, 3.42, 3.65, 3.40, 3.93, 4.39…
## $ PSA <dbl> 5.30, 1.34, 16.22, 2.71, 8.25, 2.97, 2.80, 2.95, 1.84, 1.6…
## $ WHH <dbl> 1.65, 8.50, 1.18, 2.60, 1.35, 2.50, 2.40, 2.45, 4.00, 5.00…
## $ WHD <dbl> 4.20, 5.50, 7.00, 3.50, 5.50, 3.50, 3.75, 3.50, 4.00, 4.33…
## $ WHA <dbl> 5.00, 1.33, 17.00, 2.70, 8.00, 2.80, 2.75, 2.88, 1.83, 1.6…
## $ `1XBH` <dbl> 1.68, 8.60, 1.20, 2.68, 1.37, 2.46, 2.42, 2.47, 4.02, 4.98…
## $ `1XBD` <dbl> 4.32, 5.85, 7.65, 3.66, 5.74, 3.70, 3.91, 3.66, 4.22, 4.52…
## $ `1XBA` <dbl> 5.03, 1.35, 16.00, 2.63, 8.10, 2.87, 2.81, 2.88, 1.85, 1.6…
## $ MaxH <dbl> 1.68, 9.00, 1.20, 2.76, 1.37, 2.51, 2.49, 2.52, 4.30, 5.30…
## $ MaxD <dbl> 4.50, 6.10, 9.10, 3.66, 5.90, 3.70, 3.91, 3.66, 4.22, 4.52…
## $ MaxA <dbl> 5.50, 1.37, 18.00, 2.78, 8.60, 3.00, 2.87, 2.99, 1.88, 1.6…
## $ AvgH <dbl> 1.62, 8.28, 1.18, 2.67, 1.35, 2.45, 2.44, 2.46, 4.05, 5.01…
## $ AvgD <dbl> 4.36, 5.76, 7.86, 3.41, 5.62, 3.49, 3.69, 3.44, 4.00, 4.37…
## $ AvgA <dbl> 5.15, 1.34, 15.87, 2.68, 8.10, 2.89, 2.79, 2.90, 1.83, 1.6…
## $ BFEH <dbl> 1.66, 9.40, 1.19, 2.78, 1.37, 2.54, 2.50, 2.54, 4.30, 5.40…
## $ BFED <dbl> 4.50, 6.20, 9.00, 3.50, 6.00, 3.60, 3.75, 3.55, 4.10, 4.60…
## $ BFEA <dbl> 5.60, 1.36, 18.00, 2.78, 9.20, 3.00, 2.94, 3.00, 1.87, 1.6…
## $ `B365>2.5` <dbl> 1.53, 1.40, 1.44, 1.80, 1.40, 1.73, 1.57, 1.80, 1.50, 1.53…
## $ `B365<2.5` <dbl> 2.50, 3.00, 2.75, 2.00, 3.00, 2.10, 2.38, 2.00, 2.63, 2.50…
## $ `P>2.5` <dbl> 1.56, 1.41, 1.46, 1.83, 1.40, 1.79, 1.59, 1.83, 1.52, 1.53…
## $ `P<2.5` <dbl> 2.56, 3.00, 2.79, 2.05, 3.09, 2.11, 2.46, 2.05, 2.62, 2.61…
## $ `Max>2.5` <dbl> 1.57, 1.43, 1.50, 1.85, 1.42, 1.81, 1.59, 1.85, 1.53, 1.55…
## $ `Max<2.5` <dbl> 2.60, 3.07, 2.82, 2.08, 3.12, 2.14, 2.52, 2.08, 2.69, 2.63…
## $ `Avg>2.5` <dbl> 1.53, 1.41, 1.46, 1.81, 1.40, 1.77, 1.57, 1.81, 1.50, 1.52…
## $ `Avg<2.5` <dbl> 2.52, 2.94, 2.70, 2.04, 3.01, 2.08, 2.43, 2.03, 2.59, 2.54…
## $ `BFE>2.5` <dbl> 1.59, 1.45, 1.53, 1.88, 1.43, 1.83, 1.63, 1.84, 1.56, 1.56…
## $ `BFE<2.5` <dbl> 2.64, 3.15, 2.84, 2.08, 3.15, 2.16, 2.52, 2.14, 2.68, 2.70…
## $ AHh <dbl> -1.00, 1.50, -2.00, 0.00, -1.50, 0.00, -0.25, -0.25, 0.50,…
## $ B365AHH <dbl> 2.05, 2.02, 1.93, 1.96, 1.98, 1.82, 2.08, 2.11, 2.07, 1.86…
## $ B365AHA <dbl> 1.88, 1.91, 2.00, 1.97, 1.95, 2.11, 1.73, 1.82, 1.86, 2.07…
## $ PAHH <dbl> 2.07, 1.99, 1.88, 1.96, 1.94, 1.78, 2.16, 2.15, 2.06, 1.86…
## $ PAHA <dbl> 1.86, 1.92, 2.00, 1.94, 1.96, 2.14, 1.76, 1.78, 1.85, 2.05…
## $ MaxAHH <dbl> 2.07, 2.02, 1.97, 1.97, 1.99, 1.83, 2.16, 2.15, 2.08, 1.86…
## $ MaxAHA <dbl> 1.89, 1.95, 2.00, 1.97, 1.98, 2.14, 1.82, 1.82, 1.86, 2.09…
## $ AvgAHH <dbl> 2.03, 1.97, 1.90, 1.94, 1.93, 1.80, 2.10, 2.10, 2.03, 1.83…
## $ AvgAHA <dbl> 1.85, 1.90, 1.96, 1.94, 1.93, 2.09, 1.78, 1.78, 1.83, 2.03…
## $ BFEAHH <dbl> 2.10, 2.04, 1.94, 1.99, 1.94, 1.83, 2.17, 2.18, 2.12, 1.91…
## $ BFEAHA <dbl> 1.88, 1.93, 2.00, 1.99, 2.01, 2.17, 1.84, 1.83, 1.87, 2.06…
## $ B365CH <dbl> 1.67, 8.00, 1.14, 3.10, 1.40, 2.20, 2.40, 2.88, 3.60, 4.50…
## $ B365CD <dbl> 4.10, 5.75, 8.50, 3.40, 5.00, 3.40, 3.50, 3.20, 3.90, 4.20…
## $ B365CA <dbl> 5.00, 1.33, 15.00, 2.30, 7.00, 3.25, 2.80, 2.55, 1.95, 1.7…
## $ BWCH <dbl> 1.65, 8.00, 1.16, 3.00, 1.39, 2.20, 2.50, 2.80, 3.60, 4.33…
## $ BWCD <dbl> 4.20, 5.75, 8.50, 3.40, 5.25, 3.40, 3.50, 3.20, 3.75, 4.20…
## $ BWCA <dbl> 4.80, 1.34, 18.00, 2.37, 7.25, 3.25, 2.75, 2.60, 1.98, 1.7…
## $ BFCH <dbl> 1.62, 7.50, 1.13, 3.00, 1.36, 2.20, 2.38, 2.80, 3.60, 4.33…
## $ BFCD <dbl> 4.00, 5.50, 8.50, 3.30, 5.00, 3.30, 3.40, 3.25, 3.80, 4.10…
## $ BFCA <dbl> 5.00, 1.33, 17.00, 2.30, 7.50, 3.25, 2.80, 2.63, 2.00, 1.7…
## $ PSCH <dbl> 1.65, 8.14, 1.15, 3.15, 1.42, 2.24, 2.54, 2.92, 3.86, 4.64…
## $ PSCD <dbl> 4.23, 6.09, 9.05, 3.41, 5.30, 3.50, 3.51, 3.24, 3.91, 4.33…
## $ PSCA <dbl> 5.28, 1.34, 18.76, 2.40, 7.26, 3.37, 2.86, 2.66, 1.97, 1.7…
## $ WHCH <dbl> 1.60, 8.00, 1.15, 3.10, 1.40, 2.25, 2.50, 3.00, 3.50, 4.60…
## $ WHCD <dbl> 4.20, 5.50, 8.00, 3.30, 5.00, 3.40, 3.50, 3.20, 3.90, 4.20…
## $ WHCA <dbl> 5.50, 1.35, 19.00, 2.40, 7.50, 3.25, 2.80, 2.50, 2.00, 1.7…
## $ `1XBCH` <dbl> 1.66, 8.57, 1.16, 3.16, 1.39, 2.26, 2.56, 2.98, 3.89, 4.79…
## $ `1XBCD` <dbl> 4.15, 5.85, 9.39, 3.47, 5.34, 3.49, 3.60, 3.30, 3.96, 4.27…
## $ `1XBCA` <dbl> 5.33, 1.34, 16.60, 2.34, 7.90, 3.33, 2.77, 2.56, 1.92, 1.7…
## $ MaxCH <dbl> 1.70, 8.57, 1.17, 3.16, 1.44, 2.38, 2.56, 3.00, 3.89, 4.90…
## $ MaxCD <dbl> 4.33, 6.25, 9.40, 3.50, 5.75, 3.55, 3.60, 3.40, 3.96, 4.40…
## $ MaxCA <dbl> 5.50, 1.39, 21.00, 2.45, 8.00, 3.37, 2.91, 2.72, 2.05, 1.7…
## $ AvgCH <dbl> 1.66, 7.87, 1.15, 3.06, 1.39, 2.25, 2.47, 2.85, 3.64, 4.57…
## $ AvgCD <dbl> 4.20, 5.81, 8.62, 3.40, 5.27, 3.44, 3.51, 3.28, 3.83, 4.23…
## $ AvgCA <dbl> 5.02, 1.35, 18.11, 2.38, 7.33, 3.23, 2.83, 2.62, 1.98, 1.7…
## $ BFECH <dbl> 1.72, 8.60, 1.17, 3.15, 1.43, 2.30, 2.54, 2.94, 3.75, 4.70…
## $ BFECD <dbl> 4.20, 6.20, 9.40, 3.55, 5.50, 3.50, 3.65, 3.35, 3.90, 4.40…
## $ BFECA <dbl> 5.40, 1.37, 21.00, 2.46, 8.20, 3.50, 2.98, 2.74, 2.06, 1.7…
## $ `B365C>2.5` <dbl> 1.62, 1.36, 1.40, 1.93, 1.44, 1.89, 1.73, 2.07, 1.57, 1.57…
## $ `B365C<2.5` <dbl> 2.30, 3.20, 3.00, 1.97, 2.75, 2.01, 2.10, 1.83, 2.38, 2.38…
## $ `PC>2.5` <dbl> 1.63, 1.37, 1.41, 1.93, 1.46, 1.89, 1.72, 2.09, 1.58, 1.54…
## $ `PC<2.5` <dbl> 2.38, 3.30, 2.98, 1.97, 2.85, 2.02, 2.21, 1.83, 2.51, 2.61…
## $ `MaxC>2.5` <dbl> 1.66, 1.40, 1.45, 1.95, 1.46, 1.93, 1.73, 2.09, 1.61, 1.57…
## $ `MaxC<2.5` <dbl> 2.45, 3.38, 3.00, 2.00, 3.05, 2.04, 2.32, 1.91, 2.52, 2.62…
## $ `AvgC>2.5` <dbl> 1.61, 1.37, 1.42, 1.89, 1.43, 1.87, 1.68, 2.00, 1.56, 1.54…
## $ `AvgC<2.5` <dbl> 2.37, 3.18, 2.93, 1.96, 2.84, 1.96, 2.22, 1.85, 2.45, 2.52…
## $ `BFEC>2.5` <dbl> 1.68, 1.40, 1.44, 1.94, 1.49, 1.91, 1.74, 2.10, 1.62, 1.58…
## $ `BFEC<2.5` <dbl> 2.46, 3.40, 3.20, 2.04, 2.98, 2.08, 2.32, 1.89, 2.60, 2.70…
## $ AHCh <dbl> -0.75, 1.50, -2.25, 0.25, -1.25, -0.25, 0.00, 0.00, 0.50, …
## $ B365CAHH <dbl> 1.86, 2.05, 2.02, 1.87, 1.87, 1.94, 1.83, 2.07, 1.94, 2.03…
## $ B365CAHA <dbl> 2.07, 1.88, 1.91, 2.06, 2.06, 1.99, 2.10, 1.86, 1.99, 1.90…
## $ PCAHH <dbl> 1.83, 2.04, 2.00, 1.86, 1.88, 1.94, 1.85, 2.06, 1.96, 2.02…
## $ PCAHA <dbl> 2.11, 1.90, 1.90, 2.07, 2.06, 1.98, 2.09, 1.88, 1.97, 1.91…
## $ MaxCAHH <dbl> 1.88, 2.20, 2.05, 1.92, 1.89, 2.05, 1.86, 2.11, 1.96, 2.06…
## $ MaxCAHA <dbl> 2.11, 2.00, 1.93, 2.10, 2.10, 2.00, 2.14, 1.88, 2.00, 1.92…
## $ AvgCAHH <dbl> 1.82, 1.99, 1.99, 1.83, 1.82, 1.93, 1.82, 2.05, 1.90, 1.99…
## $ AvgCAHA <dbl> 2.05, 1.88, 1.87, 2.04, 2.05, 1.93, 2.08, 1.85, 1.96, 1.87…
## $ BFECAHH <dbl> 1.90, 2.04, 2.02, 1.88, 1.89, 1.96, 1.84, 2.07, 1.93, 2.03…
## $ BFECAHA <dbl> 2.08, 1.93, 1.96, 2.11, 2.10, 2.02, 2.18, 1.92, 2.07, 1.96…vars <- c("Date","HomeTeam","AwayTeam","FTR","FTHG","FTAG","HS","AS","HC","AC","HY","AY")
df2 <- df %>% select(any_of(vars))
summary(df2$FTR)## Length Class Mode
## 380 character characterPreprocessing and item creation
Association rules require categorical items. Therefore, we discretize numerical variables using data-driven thresholds (quartiles) to ensure that items are neither too rare nor too frequent.
q_HS_hi <- quantile(df2$HS, 0.75, na.rm = TRUE)
q_AS_lo <- quantile(df2$AS, 0.25, na.rm = TRUE)
q_HC_hi <- quantile(df2$HC, 0.75, na.rm = TRUE)
q_HY_lo <- quantile(df2$HY, 0.25, na.rm = TRUE)
c(q_HS_hi = q_HS_hi, q_AS_lo = q_AS_lo, q_HC_hi = q_HC_hi, q_HY_lo = q_HY_lo)## q_HS_hi.75% q_AS_lo.25% q_HC_hi.75% q_HY_lo.25%
## 17 9 7 1Match-level items
HOME_SHOTS_HIGH (home team shots in the top quartile),
AWAY_SHOTS_LOW (away team shots in the bottom quartile),
SHOT_ADV_HOME_5PLUS (home has at least +5 shot advantage),
HOME_CORNERS_HIGH (home corners in the top quartile),
CORNER_ADV_HOME_3PLUS (home has at least +3 corner advantage),
HOME_CARDS_LOW (home yellow cards in the bottom quartile),
GOALS_3PLUS (at least 3 total goals),
and the outcome label HOME_WIN / DRAW / AWAY_WIN.
df_items <- df2 %>%
mutate(
RESULT = case_when(
FTR == "H" ~ "RESULT_HOME_WIN",
FTR == "A" ~ "RESULT_AWAY_WIN",
TRUE ~ "RESULT_DRAW"
),
GOALS_3PLUS = ifelse((FTHG + FTAG) >= 3, "GOALS_3PLUS", NA),
HOME_SHOTS_HIGH = ifelse(!is.na(HS) & HS >= q_HS_hi, "HOME_SHOTS_HIGH", NA),
AWAY_SHOTS_LOW = ifelse(!is.na(AS) & AS <= q_AS_lo, "AWAY_SHOTS_LOW", NA),
HOME_CORNERS_HIGH = ifelse(!is.na(HC) & HC >= q_HC_hi, "HOME_CORNERS_HIGH", NA),
HOME_CARDS_LOW = ifelse(!is.na(HY) & HY <= q_HY_lo, "HOME_CARDS_LOW", NA),
SHOT_ADV_HOME_5PLUS = ifelse(!is.na(HS) & !is.na(AS) & (HS - AS) >= 5,
"SHOT_ADV_HOME_5PLUS", NA),
CORNER_ADV_HOME_3PLUS = ifelse(!is.na(HC) & !is.na(AC) & (HC - AC) >= 3,
"CORNER_ADV_HOME_3PLUS", NA)
)Transactions
Each match is treated as one transaction containing the created items. This produces a transaction dataset suitable for Apriori.
df_long <- df_items %>%
mutate(match_id = row_number()) %>%
select(
match_id,
RESULT,
GOALS_3PLUS,
HOME_SHOTS_HIGH,
AWAY_SHOTS_LOW,
HOME_CORNERS_HIGH,
HOME_CARDS_LOW,
SHOT_ADV_HOME_5PLUS,
CORNER_ADV_HOME_3PLUS
) %>%
pivot_longer(-match_id, values_to = "item", values_drop_na = TRUE) %>%
distinct(match_id, item)
basket_list <- split(df_long$item, df_long$match_id)
trans <- as(basket_list, "transactions")
summary(trans)## transactions as itemMatrix in sparse format with
## 380 rows (elements/itemsets/transactions) and
## 10 columns (items) and a density of 0.3642105
##
## most frequent items:
## GOALS_3PLUS HOME_CARDS_LOW RESULT_HOME_WIN SHOT_ADV_HOME_5PLUS
## 215 166 155 137
## RESULT_AWAY_WIN (Other)
## 132 579
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8
## 45 96 64 46 49 41 32 7
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.642 5.000 8.000
##
## includes extended item information - examples:
## labels
## 1 AWAY_SHOTS_LOW
## 2 CORNER_ADV_HOME_3PLUS
## 3 GOALS_3PLUS
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3itemLabels(trans)## [1] "AWAY_SHOTS_LOW" "CORNER_ADV_HOME_3PLUS" "GOALS_3PLUS"
## [4] "HOME_CARDS_LOW" "HOME_CORNERS_HIGH" "HOME_SHOTS_HIGH"
## [7] "RESULT_AWAY_WIN" "RESULT_DRAW" "RESULT_HOME_WIN"
## [10] "SHOT_ADV_HOME_5PLUS"grep("match", itemLabels(trans), value = TRUE)## character(0)Association rule mining (Apriori)
We mine association rules where the right-hand side (RHS) is HOME_WIN. We focus on rules that are sufficiently frequent (support) and reliable (confidence), and we rank them by lift.
rules_win <- apriori(
trans,
parameter = list(supp = 0.02, conf = 0.35, minlen = 2, maxlen = 4),
appearance = list(default = "lhs", rhs = "RESULT_HOME_WIN"),
control = list(verbose = FALSE)
)
length(rules_win)## [1] 63inspect(head(sort(rules_win, by = "lift"), 15))## lhs rhs support confidence coverage lift count
## [1] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.07105263 0.7714286 0.09210526 1.891244 27
## [2] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10000000 0.7450980 0.13421053 1.826692 38
## [3] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_CARDS_LOW} => {RESULT_HOME_WIN} 0.05526316 0.7241379 0.07631579 1.775306 21
## [4] {AWAY_SHOTS_LOW,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.10263158 0.7090909 0.14473684 1.738416 39
## [5] {AWAY_SHOTS_LOW,
## HOME_SHOTS_HIGH,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10263158 0.7090909 0.14473684 1.738416 39
## [6] {AWAY_SHOTS_LOW,
## CORNER_ADV_HOME_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.07368421 0.6829268 0.10789474 1.674272 28
## [7] {AWAY_SHOTS_LOW,
## GOALS_3PLUS} => {RESULT_HOME_WIN} 0.11315789 0.6825397 0.16578947 1.673323 43
## [8] {AWAY_SHOTS_LOW,
## HOME_CORNERS_HIGH,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.06578947 0.6756757 0.09736842 1.656495 25
## [9] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.05789474 0.6666667 0.08684211 1.634409 22
## [10] {GOALS_3PLUS,
## HOME_SHOTS_HIGH,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10789474 0.6612903 0.16315789 1.621228 41
## [11] {GOALS_3PLUS,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.13947368 0.6463415 0.21578947 1.584579 53
## [12] {AWAY_SHOTS_LOW,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.15263158 0.6444444 0.23684211 1.579928 58
## [13] {AWAY_SHOTS_LOW,
## HOME_CARDS_LOW,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.04736842 0.6428571 0.07368421 1.576037 18
## [14] {GOALS_3PLUS,
## HOME_CARDS_LOW,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.06052632 0.6388889 0.09473684 1.566308 23
## [15] {GOALS_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.12368421 0.6351351 0.19473684 1.557105 47Baseline probability of a home win
base_homewin <- itemFrequency(trans, type = "relative")["RESULT_HOME_WIN"]
base_homewin## RESULT_HOME_WIN
## 0.4078947The baseline probability of a home win in the dataset is approximately 0.408. Therefore, rules with lift values between 1.6 and 1.9 represent a substantial increase over the unconditional probability of a home win.
Rule evaluation metrics
Support: fraction of matches where both the LHS conditions and the RHS outcome occur. Higher support means the pattern appears more often in the dataset.
Confidence: estimated probability of the RHS given the LHS (i.e., P(HOME_WIN∣LHS)).
Lift: how much more likely the RHS becomes when the LHS occurs compared to the baseline probability of the RHS. Lift > 1 indicates a positive association; the larger the lift, the stronger the association.
Rules
Rule 1 (highest lift ~1.89)
{AWAY_SHOTS_LOW, GOALS_3PLUS, HOME_SHOTS_HIGH} ⇒ {RESULT_HOME_WIN}
Support ≈ 0.071 (27 matches): the pattern occurs in about 7% of all matches.
Confidence ≈ 0.77: when the away team has low shots, the home team has high shots, and the match has 3+ total goals, the home team wins ~77% of the time.
Lift ≈ 1.89: this is almost twice as likely as a home win in a random match. Football interpretation: a combination of strong home attacking volume + weak away attacking output, in a more open match, strongly favors a home win.
Rule 2 (support 10%, confidence 0.75)
{AWAY_SHOTS_LOW, GOALS_3PLUS, SHOT_ADV_HOME_5PLUS} ⇒ {RESULT_HOME_WIN}
Appears in 38 matches (10%), with ~75% success. Interpretation: when home shot advantage is large and away shooting is low, home wins are very frequent—especially in matches with 3+ goals.
Rule 4 (simple, interpretable)
{AWAY_SHOTS_LOW, HOME_SHOTS_HIGH} ⇒ {RESULT_HOME_WIN}
Support ≈ 0.103 (39 matches) and confidence ≈ 0.71. Interpretation: even without adding goals/corners/cards, the “high home shots + low away shots” combination alone is a strong signal of a home win.
Rule 11–12 (dominance signal)
{GOALS_3PLUS, SHOT_ADV_HOME_5PLUS} ⇒ {RESULT_HOME_WIN} (lift ~1.58) {AWAY_SHOTS_LOW, SHOT_ADV_HOME_5PLUS} ⇒ {RESULT_HOME_WIN} (lift ~1.58) Interpretation: shot dominance is a recurring driver across many high-lift rules, suggesting that shot-based items capture a core “control of the match” dimension associated with home wins.
Rule pruning (removing redundancy)
Raw rule sets can contain many redundant rules. We remove redundant rules and keep more informative patterns.
rules_clean <- rules_win[!is.redundant(rules_win)]
rules_clean <- sort(rules_clean, by = "lift", decreasing = TRUE)
summary(rules_clean)## set of 28 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3 4
## 7 13 8
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.750 3.000 3.036 4.000 4.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.05000 Min. :0.4157 Min. :0.07632 Min. :1.019
## 1st Qu.:0.09145 1st Qu.:0.5228 1st Qu.:0.15197 1st Qu.:1.282
## Median :0.10658 Median :0.5809 Median :0.17895 Median :1.424
## Mean :0.12190 Mean :0.5870 Mean :0.21833 Mean :1.439
## 3rd Qu.:0.15263 3rd Qu.:0.6449 3rd Qu.:0.27632 3rd Qu.:1.581
## Max. :0.27632 Max. :0.7714 Max. :0.56579 Max. :1.891
## count
## Min. : 19.00
## 1st Qu.: 34.75
## Median : 40.50
## Mean : 46.32
## 3rd Qu.: 58.00
## Max. :105.00
##
## mining info:
## data ntransactions support confidence
## trans 380 0.02 0.35
## call
## apriori(data = trans, parameter = list(supp = 0.02, conf = 0.35, minlen = 2, maxlen = 4), appearance = list(default = "lhs", rhs = "RESULT_HOME_WIN"), control = list(verbose = FALSE))inspect(head(rules_clean, 15))## lhs rhs support confidence coverage lift count
## [1] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.07105263 0.7714286 0.09210526 1.891244 27
## [2] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10000000 0.7450980 0.13421053 1.826692 38
## [3] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_CARDS_LOW} => {RESULT_HOME_WIN} 0.05526316 0.7241379 0.07631579 1.775306 21
## [4] {AWAY_SHOTS_LOW,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.10263158 0.7090909 0.14473684 1.738416 39
## [5] {AWAY_SHOTS_LOW,
## GOALS_3PLUS} => {RESULT_HOME_WIN} 0.11315789 0.6825397 0.16578947 1.673323 43
## [6] {GOALS_3PLUS,
## HOME_SHOTS_HIGH,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10789474 0.6612903 0.16315789 1.621228 41
## [7] {GOALS_3PLUS,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.13947368 0.6463415 0.21578947 1.584579 53
## [8] {AWAY_SHOTS_LOW,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.15263158 0.6444444 0.23684211 1.579928 58
## [9] {GOALS_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.12368421 0.6351351 0.19473684 1.557105 47
## [10] {AWAY_SHOTS_LOW,
## HOME_CARDS_LOW,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.05000000 0.6129032 0.08157895 1.502601 19
## [11] {AWAY_SHOTS_LOW,
## HOME_CARDS_LOW} => {RESULT_HOME_WIN} 0.10263158 0.6093750 0.16842105 1.493952 39
## [12] {CORNER_ADV_HOME_3PLUS,
## GOALS_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.09210526 0.6034483 0.15263158 1.479422 35
## [13] {AWAY_SHOTS_LOW,
## CORNER_ADV_HOME_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.08947368 0.5862069 0.15263158 1.437152 34
## [14] {HOME_CARDS_LOW,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10263158 0.5820896 0.17631579 1.427058 39
## [15] {GOALS_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.10526316 0.5797101 0.18157895 1.421225 40Visualization
We visualize rules using standard arulesViz plots. These help assess the trade-off between support, confidence, and lift.
plot(rules_clean, measure = c("support", "lift"), shading = "confidence")
plot(rules_clean, method = "grouped")
plot(head(rules_clean, 30), method = "graph", control = list(type = "items"))## Available control parameters (with default values):
## layout = stress
## circular = FALSE
## ggraphdots = NULL
## edges = <environment>
## nodes = <environment>
## nodetext = <environment>
## colors = c("#EE0000FF", "#EEEEEEFF")
## engine = ggplot2
## max = 100
## verbose = FALSE
The scatter and grouped visualizations confirm that high-lift rules tend to combine multiple indicators of home dominance, while single-item rules usually achieve lower lift values.
Interpretation
We interpret the strongest rules (highest lift) in football terms. For example, patterns involving high home shot volume and strong shot/corner advantages should be positively associated with winning at home. High-lift rules typically combine multiple indicators of home dominance, suggesting that no single statistic alone explains home wins, but rather their joint occurrence.
top_rules <- head(rules_clean, 20)
inspect(top_rules)## lhs rhs support confidence coverage lift count
## [1] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.07105263 0.7714286 0.09210526 1.891244 27
## [2] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10000000 0.7450980 0.13421053 1.826692 38
## [3] {AWAY_SHOTS_LOW,
## GOALS_3PLUS,
## HOME_CARDS_LOW} => {RESULT_HOME_WIN} 0.05526316 0.7241379 0.07631579 1.775306 21
## [4] {AWAY_SHOTS_LOW,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.10263158 0.7090909 0.14473684 1.738416 39
## [5] {AWAY_SHOTS_LOW,
## GOALS_3PLUS} => {RESULT_HOME_WIN} 0.11315789 0.6825397 0.16578947 1.673323 43
## [6] {GOALS_3PLUS,
## HOME_SHOTS_HIGH,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10789474 0.6612903 0.16315789 1.621228 41
## [7] {GOALS_3PLUS,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.13947368 0.6463415 0.21578947 1.584579 53
## [8] {AWAY_SHOTS_LOW,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.15263158 0.6444444 0.23684211 1.579928 58
## [9] {GOALS_3PLUS,
## HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.12368421 0.6351351 0.19473684 1.557105 47
## [10] {AWAY_SHOTS_LOW,
## HOME_CARDS_LOW,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.05000000 0.6129032 0.08157895 1.502601 19
## [11] {AWAY_SHOTS_LOW,
## HOME_CARDS_LOW} => {RESULT_HOME_WIN} 0.10263158 0.6093750 0.16842105 1.493952 39
## [12] {CORNER_ADV_HOME_3PLUS,
## GOALS_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.09210526 0.6034483 0.15263158 1.479422 35
## [13] {AWAY_SHOTS_LOW,
## CORNER_ADV_HOME_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.08947368 0.5862069 0.15263158 1.437152 34
## [14] {HOME_CARDS_LOW,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.10263158 0.5820896 0.17631579 1.427058 39
## [15] {GOALS_3PLUS,
## HOME_CORNERS_HIGH} => {RESULT_HOME_WIN} 0.10526316 0.5797101 0.18157895 1.421225 40
## [16] {HOME_SHOTS_HIGH,
## SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.14473684 0.5729167 0.25263158 1.404570 55
## [17] {AWAY_SHOTS_LOW} => {RESULT_HOME_WIN} 0.17894737 0.5714286 0.31315789 1.400922 68
## [18] {CORNER_ADV_HOME_3PLUS,
## GOALS_3PLUS} => {RESULT_HOME_WIN} 0.10526316 0.5633803 0.18684211 1.381190 40
## [19] {SHOT_ADV_HOME_5PLUS} => {RESULT_HOME_WIN} 0.20000000 0.5547445 0.36052632 1.360019 76
## [20] {HOME_SHOTS_HIGH} => {RESULT_HOME_WIN} 0.16578947 0.5526316 0.30000000 1.354839 63Conclusion
Association rule mining provides an interpretable way to discover match-statistics combinations that are strongly associated with a home win. The strongest rules typically involve indicators of home dominance, such as high shooting volume or large shot/corner advantages. This approach can be extended by adding more features (e.g., odds, red cards) or by mining rules for other outcomes such as draws or away wins.
References
Data source: football-data.co.uk (Premier League match statistics CSV).