FPL Lineup Optimizer

These Fantasy Premier League lineup optimisers optimise a lineup based on some constraints and a given objective. It currently uses the 2020/2021 season's player prices and the 2019/2020 season's points scored, and maximises the total points of all players in a lineup while staying within a specified budget and conforming to other FPL rules and constraints defined by the user.

With Transfers

The with_transfers/ folder contains instructions on how to get gameweek by gameweek data, and contains an optimiser that optimises points scored over these gameweeks, while allowing for transfers. This optimiser is an extended version of optimiser_B.py, and contains many of the same parameters.

Difference Between optimiser_A and optimiser_B

In optimiser_A, you can specify the teamsize and the optimiser will attempt to maximise points for all players.

In optimiser_B, the teamsize is always 15, however you can specify a bench_strength that signifies what importance should be given to points on the bench. The optimiser maximises (1-bench_strength)*(starting 11 points) + bench_strength*(bench points). In the returned dataframe, the first 11 players are the optimised lineup's 11 starting players.

NOTE: This optimiser will only be as good as the data supplied to it - using last seasons's points scored is unlikely to be predictive of how many points players will score this season. If you want to use this to select a team, then it is highly recommended that you alter the points column in players_data.csv to something that is more predictive, such as week-on-week expected points.

Usage

1. Clone this repository - git clone "https://github.com/ChrisMusson/FPL_Optimiser"
2. Install requirements from txt file - pip install -r requirements.txt
3. Run get_data.py. This will create a players_data.csv file containing information about every player in FPL this season, along with their points so far this season. If you want to optimise something other than points so far, then you can replace this final column with something else - the optimiser will still run so long as the csv file stays in the same format and the column headers remain unchanged.
4. Open optimiser_A.py or optimiser_B.py and change the arguments to the optimise function (a list of all available arguments can be found in the function's docstring). Run the program, and a dataframe containing the optimised lineup will be printed to the command line.

Examples

optimiser_A:

Input:

optimise(
    budget=81,
    teamsize=11,
    in_team=["Salah", "Vinagre", 259],
    out_team=["Lundstram", 1],
    GK=1,
    MID=4
    )

Output:

	id	team	pos	name	cost	points
1	96	Burnley	G	Pope	5.5	170
2	259	Liverpool	D	Alexander-Arnold	7.5	210
3	250	Liverpool	D	van Dijk	6.5	178
4	457	Wolves	D	Doherty	6	167
5	81	Burnley	D	Tarkowski	5.5	143
6	471	Wolves	D	Vinagre	4.5	32
7	272	Man City	M	De Bruyne	11.5	251
8	254	Liverpool	M	Salah	12	233
9	478	Arsenal	M	Willian	8	168
10	89	Burnley	M	Westwood	5.5	118
11	366	Southampton	F	Ings	8.5	198

Total cost: £81.0m
Total points: 1868

optimiser_B

Input

optimise(
    DEF=4,
    in_team=["van Dijk", "vinagre", "jimenez"],
    out_team=["Lundstram"],
    banned_teams=["Burnley", "Aston Villa", "Man Utd"]
)

Output

	id	team	pos	name	cost	points
1	70	Brighton	G	Ryan	4.5	135
2	259	Liverpool	D	Alexander-Arnold	7.5	210
3	255	Liverpool	D	Robertson	7	181
4	250	Liverpool	D	van Dijk	6.5	178
5	457	Wolves	D	Doherty	6	167
6	272	Man City	M	De Bruyne	11.5	251
7	275	Man City	M	Mahrez	8.5	175
8	478	Arsenal	M	Willian	8	168
9	366	Southampton	F	Ings	8.5	198
10	460	Wolves	F	Jiménez	8.5	194
11	140	Crystal Palace	F	Ayew	6	132
12	429	West Ham	G	Martin	4	20
13	471	Wolves	D	Vinagre	4.5	32
14	55	Brighton	M	Stephens	4.5	63
15	364	Southampton	M	Oriol Romeu	4.5	51

Total cost: £100.0m
Total points: 1989 (+166 on the bench)