Home advantage in Fantasy Premier League
Home advantage exists in FPL: players score more FPL points when their team plays at home than when their team plays away.
FPL home advantage did not exist in the 2020/21 behind-closed-doors season.
Almost all FPL point-scoring actions exhibit home advantage: when playing at home, players register more goals, assists, clean sheets, and bonus points, concede fewer goals, and receive fewer yellow cards.
FPL home advantage exists across all player positions (goalkeeper, defender, midfielder, and forward).
Introduction
Home advantage is the tendency of teams to perform better when playing at home than they do when playing away. Home advantage has been documented across many sports, including soccer, basketball, cricket, hockey, rugby, and American football (Chicago Booth Review).
In soccer, home advantage has been shown to exist in English, German, Italian, French, and Spanish leagues. Home advantage was greatly reduced in the 2020/21 season, when many matches were played behind closed doors (i.e., without supporters in attendance) due to the COVID-19 pandemic (Psychology of Sport and Exercise).
Fantasy Premier League (FPL) is an online fantasy football game, in which participants assemble virtual teams of Premier League footballers and score points based on these players’ performances in Premier League matches. Participants in FPL (managers) select a squad of 15 players within a budget and choose 11 players from the squad to play in each gameweek (where a "gameweek" corresponds roughly to a round of Premier League matches). Points are scored for a variety of actions, including goals, assists, clean sheets, and bonus points. Managers can make transfers and substitutions throughout the season.
It is conventional wisdom among FPL managers that, because home advantage exists in soccer at a team level, home advantage also exists in FPL at the level of individual players. In this note, I explore whether this is actually the case. For a step-by-step description of what I did, see the corresponding R notebook.
Data
I use three datasets:- Vaastav Anand’s FPL Historical Dataset for data on FPL player/gameweek pairs from 2016/17 through 2022/23.
- Match results from Football-Data.co.uk.
- Data on the number of managers per season from allaboutfpl.com.
Findings
Home advantage
Home advantage exists in FPL: players score more FPL points when their team plays at home than when their team plays away. Setting aside the 2020/21 and 2021/22 seasons, players score an average of 3.3-4.5 FPL points when playing at home and score only an average of 2.7-3.2 when playing away. Home advantage in FPL points was nonexistent in the 2020/21 season (when many matches were played without crowds) and continued to be lower than normal in 2021/22.
Note that in the analysis above and in what follows, I exclude all player/gameweek observations where the player was selected in their squad by fewer than 0.5% of managers. I make this exclusion because the FPL game includes many players (such as third choice goalkeepers and youth players) who register very few minutes of play, if any, during the course of a season. By excluding players with low selection rates, I attempt to exclude these low-minutes players, who tend to have very little impact on managers' FPL scores.
Sources of home advantage
FPL points are based on a variety of different point-scoring actions, listed in the table below.
FPL point-scoring action | Points |
---|---|
Played 0-59 minutes | 1 |
Played 60+ minutes | 2 |
Each goal scored (GK / DEF / MID / FWD) | 6 / 6 / 5 / 4 |
Each assist | 3 |
Every 2 goals conceded (GK / DEF) | -1 |
Clean sheet (GK / DEF / MID) | 6 / 6 / 1 |
Every 3 saves (GK) | 2 |
Each yellow card | -1 |
Each red card | -3 |
Each penalty saved (GK) | 5 |
Each penalty missed | -2 |
Each own goal | -2 |
Bonus points | 1-3 |
Almost all point-scoring actions in FPL contribute to home advantage in total points. There are significant differences between home and away matches in all plotted point-scoring actions except for red cards. (I omit Penalties missed and Penalties saved from the plot on account of their rarity.)
Interestingly, the average number of minutes played (per gameweek/player) is slightly higher for home games than for away games. The difference is small, however—approximately 68.5 minutes for home games vs 67.75 minutes for away games—and is therefore unlikely to be meaningful for FPL points.
Unsurprisingly, given that all home advantage exists for almost all point-scoring actions, it is also the case that home advantage exists for all four player positions (goalkeeper, defender, midfielder, and forward).
Do FPL managers take advantage of home advantage?
Given that home advantage exists in FPL, we might expect FPL managers to be more likely to select players with home fixtures than players with away fixtures. However, at least for managers’ squad selection decisions, this is not the case: FPL managers are not more likely to select players with home fixtures in their squads of 15.
Of course, ideally we would also account for the selection of players in each manager's first team of 11 players each gameweek, and not just the selection of players in the squad of 15 players.
Every gameweek, managers must choose 11 players from their squad of 15 to “play” in that gameweek (i.e., to score points for the manager's team). It seems likely that managers might take home advantage into account more in the playing/benching decisions than in squad selection decisions. Managers are restricted to a single ‘free’ transfer each gameweek—subsequent transfers cost 4 points each—meaning that squads change slowly over time and players are typically held by managers for multiple gameweeks, during which the players are likely to play both home and away matches. Thus, it is potentially difficult for managers to take home and away fixtures into account when selecting players for their squads of 15.
However, our FPL dataset includes data only on selection in the squad of 15 and not on selection in each manager’s first 11 every gameweek, so analysis of the playing/benching decision is not possible, given the data.