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FPLOptimiseR

Installation

You can install FPLOptimiseR from github with:

# install.packages("devtools")
devtools::install_github('Chrisjb/fploptimiser')

Usage

fetch data

Get the historic player data from the FPL API for your own analysis

library(FPLOptimiseR)

df <- fetch_player_data()

Get expected points (adjusting for xG, xA and xCS) data from understat

df_xp <- fetch_xg_data()

Get expected clean sheets calculated with understat data:

Xclean_sheets <- fetch_xCS()
team xCS_per_game_h xCS_per_game_a total_xCS value
Leicester 0.44 (8) 0.34 (8) 6.2 7 (0.8)
Liverpool 0.4 (8) 0.37 (8) 6.2 3 (-3.2)
Manchester City 0.4 (8) 0.31 (8) 5.6 5 (-0.6)
Manchester United 0.34 (8) 0.35 (8) 5.5 2 (-3.5)
Wolverhampton Wanderers 0.28 (8) 0.34 (8) 5.0 4 (-1)
Everton 0.32 (8) 0.25 (8) 4.6 3 (-1.6)
Tottenham 0.4 (8) 0.16 (8) 4.5 2 (-2.5)
Watford 0.32 (8) 0.23 (8) 4.3 4 (-0.3)
Sheffield United 0.36 (8) 0.18 (8) 4.3 5 (0.7)
Chelsea 0.42 (8) 0.12 (8) 4.3 3 (-1.3)
Burnley 0.3 (8) 0.22 (8) 4.2 5 (0.8)
Brighton 0.34 (8) 0.18 (8) 4.1 4 (-0.1)
Arsenal 0.16 (8) 0.31 (8) 3.7 2 (-1.7)
Crystal Palace 0.34 (8) 0.12 (8) 3.7 6 (2.3)
Bournemouth 0.24 (8) 0.2 (8) 3.5 0 (-3.5)
Southampton 0.2 (8) 0.2 (8) 3.2 2 (-1.2)
Aston Villa 0.22 (8) 0.09 (8) 2.5 3 (0.5)
Norwich 0.16 (8) 0.15 (8) 2.5 2 (-0.5)
Newcastle United 0.18 (8) 0.11 (8) 2.3 4 (1.7)
West Ham 0.11 (8) 0.07 (8) 1.4 4 (2.6)

optimise team

Optimise your team for a given formation, team value, and assumed bench value. You'll want to have chosen your bench already (there is little point in optimising players on your bench as they will not earn you points a lot of the time).

You can optimise your team based on maximising total points from the historic dataset (full season by default) or points per game ('points' or 'ppg' respectively). If using 'ppg' you will want to set a min_games value so that players with high points per game but with relatively few games played are avoided.

result <- optimise_team(objective = 'ppg', bank = 1000, bench_value = 170, gk = 1, def = 3, mid = 4, fwd = 3, min_games = 3)

Using expected points

If we want to use expected points instead of actual points to optimise the team, we should first fetch the xg data and then we can feed that into the custom_df argument to optimise_team:

df_xp <- fetch_xg_data()

result_xp <- optimise_team(objective = 'ppg', bank = 1000, bench_value = 170, gk = 1, def = 3, mid = 4, fwd = 3, min_games = 3, custom_df = df_xp)

Getting fixtures

We can get and visualise fixtures for the next n games using:

fixtures <- fetch_fixtures(n = 10)
time gw team_id team_name ha played opponent difficulty mean_difficulty median_difficulty
2021-08-13 1 1 Arsenal a FALSE Brentford 2 2.8 2.5
2021-08-14 1 5 Burnley h FALSE Brighton 2 2.9 3.0
2021-08-14 1 4 Brighton a FALSE Burnley 2 2.9 2.5
2021-08-14 1 6 Chelsea h FALSE Crystal Palace 2 2.8 2.0
2021-08-14 1 8 Everton h FALSE Southampton 2 2.7 3.0
2021-08-14 1 9 Leicester h FALSE Wolves 2 2.8 2.5
2021-08-14 1 18 Watford h FALSE Aston Villa 2 2.7 2.5
2021-08-14 1 2 Aston Villa a FALSE Watford 2 3.1 3.5
2021-08-14 1 11 Liverpool a FALSE Norwich 2 2.7 2.0
2021-08-15 1 19 West Ham a FALSE Newcastle 2 2.8 3.0

We can visualise the resulting fixture list:

plot(fixtures)

It may be useful to know teams which have relatively uncorrelated fixtures (good for rotating substitutes):

fixtures <- fixture_rotation(fixtures)
team1 team2 cor
Liverpool Crystal Palace 0.6755111
Crystal Palace Leeds 0.6755111
Chelsea Crystal Palace 0.6805447
Chelsea Newcastle 0.6998542
Chelsea Everton 0.7013344
Everton Liverpool 0.7114582
Everton Leeds 0.7114582
Burnley Chelsea 0.7356619
Brighton Newcastle 0.7419985
Aston Villa Liverpool 0.7419985

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