mirror of
https://github.com/karl0ss/football-manager-squad-and-recruitment.git
synced 2025-05-25 23:45:17 +01:00
added position calculator with logic, added pre-commit linting, updated requirements and readme, added attribute rating for position calculator
This commit is contained in:
jake-fawcett
parent
4eefd5f29f
commit
574cb795d8
46
.pre-commit-config.yaml
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46
.pre-commit-config.yaml
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@ -0,0 +1,46 @@
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.4.0
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hooks:
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- id: check-added-large-files
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args: [--maxkb=2500]
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- id: check-ast
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- id: check-case-conflict
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- id: check-docstring-first
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- id: check-json
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- id: check-merge-conflict
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- id: check-toml
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- id: check-yaml
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- id: end-of-file-fixer
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- id: name-tests-test
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args: [--pytest-test-first]
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# - id: no-commit-to-branch
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# args: [--branch, main]
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- repo: https://github.com/pycqa/isort
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rev: 5.12.0
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hooks:
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- id: isort
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name: isort (python)
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args: ["--profile", "black"]
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- repo: https://github.com/pre-commit/mirrors-yapf
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rev: v0.32.0
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hooks:
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- id: yapf
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additional_dependencies: [toml]
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args: [--style "google" ]
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- repo: https://github.com/psf/black
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rev: 23.1.0
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hooks:
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- id: black
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args: [--line-length, '180']
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- repo: https://github.com/PyCQA/flake8
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rev: 6.0.0
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hooks:
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- id: flake8
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args: [--docstring-convention, google, --max-line-length, '180', --ignore, 'D100,D101,D102,D103,D104']
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additional_dependencies: [flake8-bugbear, flake8-docstrings, pydocstyle==6.1.1]
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- repo: https://github.com/pycqa/bandit
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rev: 1.7.4
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hooks:
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- id: bandit
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args: [--skip, B608]
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15
README.md
15
README.md
@ -1,9 +1,16 @@
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# Football Manager player role evaluation
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Python code to evaluate Player attributes in Football Manager against roles based on role weightings. Work in progress.
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Python code to evaluate Player attributes in Football Manager against roles based on role weightings. Work in progress. Inspired by squirrel_plays_FOF's video [FM24 player recruitment using python](https://www.youtube.com/watch?v=hnAuOakqR90)
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Inspired by squirrel_plays_FOF's video [FM24 player recruitment using python](https://www.youtube.com/watch?v=hnAuOakqR90)
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This is split into two scripts; position_score_calculator.py which calculates score based on positions ([Inspired by Mark on fm-arena](https://fm-arena.com/thread/1949-fm22-positional-filters-what-are-the-best-attributes-for-each-position/)) and role_score_calculator.py which caculates scores based on roles (this is missing plenty of roles at the moment!).
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## Usage
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position_score_calculator.py:
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```
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python3 role_score_calculator.py --input-filepath "squad.html" --output-filepath "squad_output.html" --roles gk fb dm w iw
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```
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role_score_calculator.py:
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```
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python3 position_score_calculator.py --input-filepath "squad.html" --output-filepath "squad_output.html"
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```
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python3 position_score_calculator.py --input-filepath "squad.html" --output-filepath "squad_output.html" --roles gk fb dm w iw
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```
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BIN
attribute_ratings.xlsx
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BIN
attribute_ratings.xlsx
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Binary file not shown.
@ -1,91 +1,35 @@
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import pandas as pd
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import argparse
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# Define Player attributes
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# TODO: Add roles.
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gk = {
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"role_name": "gk",
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"primary_multiplier": 5,
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"primary_attributes": ["Agi", "Ref"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"]
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}
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import pandas as pd
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fb = {
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"role_name": "fb",
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"primary_multiplier": 5,
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"primary_attributes": ["Wor", "Acc", "Pac", "Sta"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Cro", "Dri", "Mar", "OtB", "Tck", "Tea"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Agi", "Ant", "Cnt", "Dec", "Fir", "Pas", "Pos", "Tec"]
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}
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cd = {
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"role_name": "cd",
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"primary_multiplier": 3,
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"primary_attributes": ["Cmp", "Hea", "Jum", "Mar", "Pas", "Pos", "Str", "Tck", "Pac"],
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"secondary_multiplier": 1,
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"secondary_attributes": ["Agg", "Ant", "Bra", "Cnt", "Dec", "Fir", "Tec", "Vis"]
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}
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dm = {
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"role_name": "dm",
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"primary_multiplier": 5,
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"primary_attributes": ["Wor", "Pac", "Sta", "Pas"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Tck", "Ant", "Cnt", "Pos", "Bal", "Agi"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Tea", "Fir", "Mar", "Agg", "Cmp", "Dec", "Str"]
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}
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b2b = {
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"role_name": "b2b",
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"primary_multiplier": 5,
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"primary_attributes": ["Pas", "Wor", "Sta"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Tck", "OtB", "Tea", "Vis", "Str", "Dec", "Pos", "Pac"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Agg", "Ant", "Fin", "Lon", "Cmp", "Acc", "Bal", "Fir", "Dri", "Tec"]
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}
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w = {
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"role_name": "w",
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"primary_multiplier": 3,
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"primary_attributes": ["Acc", "Cro", "Dri", "OtB", "Pac", "Tec"],
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"secondary_multiplier": 1,
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"secondary_attributes": ["Agi", "Fir", "Pas", "Sta", "Wor"],
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}
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iw = {
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"role_name": "iw",
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"primary_multiplier": 5,
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"primary_attributes": ["Acc", "Pac", "Wor"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Dri", "Pas", "Tec", "OtB"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Cro", "Fir", "Cmp", "Dec", "Vis", "Agi", "Sta"]
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}
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def load_xlsx_data_to_dataframe(filepath: str) -> pd.DataFrame:
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"""Read XLSX file into a Dataframe.
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Keyword arguments:
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filepath -- path to xlsx file
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"""
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df = pd.read_excel(filepath, engine="openpyxl", nrows=12)
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return df
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def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame:
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"""Read HTML file exported by FM into a Dataframe
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"""Read HTML file exported by FM into a Dataframe.
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Keyword arguments:
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filepath -- path to fm player html file
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"""
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player_df = pd.read_html(filepath, header=0, encoding="utf-8", keep_default_na=False)[0]
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df = pd.read_html(filepath, header=0, encoding="utf-8", keep_default_na=False)[0]
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# Clean Dataframe to get rid of unknown values and ability ranges (takes the lowest value)
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# This casts to a string to be able to split, so we have to cast back to an int later.
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player_df = player_df.replace("-", 0)
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player_df = player_df.map(lambda x: str(x).split("-")[0])
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return player_df
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df = df.replace("-", 0)
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df = df.map(lambda x: str(x).split("-")[0])
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return df
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def export_html_from_dataframe(player_df: pd.DataFrame, filepath: str) -> str:
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"""Export Dataframe as html with jQuery Data Tables
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"""Export Dataframe as html with jQuery Data Tables.
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Taken from: https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python.
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Keyword arguments:
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@ -115,71 +59,43 @@ def export_html_from_dataframe(player_df: pd.DataFrame, filepath: str) -> str:
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"""
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open(filepath, "w", encoding="utf-8").write(html)
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# TODO: Do I even want this?
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def calc_composite_scores(player_df: pd.DataFrame) -> pd.DataFrame:
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"""Calculate Speed, Workrate and Set Piece scores
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def calc_role_scores(player_df: pd.DataFrame, attribute_df: pd.DataFrame) -> pd.DataFrame:
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"""Calculate Player position scores based on selected attribute weightings.
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Keyword arguments:
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player_df: Dataframe of Players and Attributes
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player_df: Dataframe of Players and their Attributes
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attribute_df: Dataframe of Attributes and their Weightings
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"""
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player_df['Spd'] = ( player_df['Pac'] + player_df['Acc'] ) / 2
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player_df['Work'] = ( player_df['Wor'] + player_df['Sta'] ) / 2
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player_df['SetP'] = ( player_df['Jum'] + player_df['Bra'] ) / 2
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for _, weightings in attribute_df.iterrows():
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role = weightings["Ratings Weights"]
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player_df[role] = 0
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for attribute in weightings.index[1:]:
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weighting = weightings[attribute]
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try:
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player_df[role] += round(pd.to_numeric(player_df[attribute]) * weighting / 20, 2)
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except Exception as e: # Used to Nat being used twice (Nationality and Natural Fitness)
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print(e)
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continue
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return player_df
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def sum_attributes(player_df: pd.DataFrame, role: str, attribute_type: str, attributes: [str]) -> pd.DataFrame:
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"""Create a new Column containing the sum of provided attribute columns
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Keyword arguments:
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player_df: Dataframe of Players and Attributes
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role: Name of role to be used as additional column in dataframe
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attribute_type: Type of Attribute [Primary, Secondary, Tertiary]
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attributes: List of Attributes to Sum
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"""
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player_df[f'{role}_{attribute_type}'] = 0
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for attribute in attributes:
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player_df[f'{role}_{attribute_type}'] += pd.to_numeric(player_df[attribute])
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player_df[f'{role}_{attribute_type}'] = round(player_df[f'{role}_{attribute_type}'] / len(attributes), 2)
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return player_df
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def calc_role_scores(player_df: pd.DataFrame, role: dict) -> pd.DataFrame:
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"""Calculate Player Role scores based on selected attributes.
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Keyword arguments:
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player_df: Dataframe of Players and Attributes
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role: Dictionary containing role name, role attributes and role attribute weightings
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"""
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player_df = sum_attributes(player_df, role["role_name"], "primary", role["primary_attributes"])
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player_df = sum_attributes(player_df, role["role_name"], "secondary", role["secondary_attributes"])
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if "tertiary_attributes" in role:
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print("here")
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player_df = sum_attributes(player_df, role["role_name"], "tertiary", role["tertiary_attributes"])
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divisor = role["primary_multiplier"] + role["secondary_multiplier"] + role["tertiary_multiplier"]
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player_df[f'{role["role_name"]}'] = round((((player_df[f'{role["role_name"]}_primary'] * 5) + (player_df[f'{role["role_name"]}_secondary'] * 3) + (player_df[f'{role["role_name"]}_tertiary'] * 1)) / divisor ), 2)
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return player_df
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def calc_role_scores_for_tactic_roles(player_df: pd.DataFrame, tactic_roles: [dict]):
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for role in tactic_roles:
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player_df = calc_role_scores(player_df, role)
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return player_df
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if __name__ == "__main__":
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# Parse Input args
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--input-filepath", type=str, help="Path to Input Html file")
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parser.add_argument("-o", "--output-filepath", type=str, help="Path to Export resultant Html file")
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parser.add_argument("-r", "--roles", nargs='+', type=str, help="Space seperated list of roles for Evaluation")
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parser.add_argument("-a", "--attribute-filepath", type=str, help="Path to Attribute XLSX file", default="./attribute_ratings.xlsx")
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args = parser.parse_args()
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input_filepath = args.input_filepath
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output_filepath = args.output_filepath
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roles = args.roles
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attribute_filepath = args.attribute_filepath
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# Take Role arg and convert to list of role dictionaries
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tactic_roles = []
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for role in roles:
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tactic_roles.append(globals()[role])
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# Inport data, calculate scores for role, export results as html
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# Inport data, calculate scores for role,
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attribute_df = load_xlsx_data_to_dataframe(attribute_filepath)
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player_df = load_html_data_to_dataframe(input_filepath)
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player_df = calc_role_scores_for_tactic_roles(player_df, tactic_roles)
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player_df = calc_role_scores(player_df, attribute_df)
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# trim attributes from final output
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player_df = player_df.drop(player_df.columns[15:-11], axis=1)
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# export results as html
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export_html_from_dataframe(player_df, output_filepath)
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@ -1,2 +1,5 @@
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pandas
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lxml
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numpy
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lxml
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openpyxl
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pre-commit
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192
role_score_calculator.py
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192
role_score_calculator.py
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import argparse
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import pandas as pd
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# Define Player attributes
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# TODO: Add roles.
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gk = {
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"role_name": "gk",
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"primary_multiplier": 5,
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"primary_attributes": ["Agi", "Ref"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"],
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}
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fb = {
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"role_name": "fb",
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"primary_multiplier": 5,
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"primary_attributes": ["Wor", "Acc", "Pac", "Sta"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Cro", "Dri", "Mar", "OtB", "Tck", "Tea"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Agi", "Ant", "Cnt", "Dec", "Fir", "Pas", "Pos", "Tec"],
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}
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cd = {
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"role_name": "cd",
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"primary_multiplier": 3,
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"primary_attributes": ["Cmp", "Hea", "Jum", "Mar", "Pas", "Pos", "Str", "Tck", "Pac"],
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"secondary_multiplier": 1,
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"secondary_attributes": ["Agg", "Ant", "Bra", "Cnt", "Dec", "Fir", "Tec", "Vis"],
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}
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dm = {
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"role_name": "dm",
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"primary_multiplier": 5,
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"primary_attributes": ["Wor", "Pac", "Sta", "Pas"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Tck", "Ant", "Cnt", "Pos", "Bal", "Agi"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Tea", "Fir", "Mar", "Agg", "Cmp", "Dec", "Str"],
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}
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b2b = {
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"role_name": "b2b",
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"primary_multiplier": 5,
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"primary_attributes": ["Pas", "Wor", "Sta"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Tck", "OtB", "Tea", "Vis", "Str", "Dec", "Pos", "Pac"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Agg", "Ant", "Fin", "Lon", "Cmp", "Acc", "Bal", "Fir", "Dri", "Tec"],
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}
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w = {
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"role_name": "w",
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"primary_multiplier": 3,
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"primary_attributes": ["Acc", "Cro", "Dri", "OtB", "Pac", "Tec"],
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"secondary_multiplier": 1,
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"secondary_attributes": ["Agi", "Fir", "Pas", "Sta", "Wor"],
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}
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iw = {
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"role_name": "iw",
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"primary_multiplier": 5,
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"primary_attributes": ["Acc", "Pac", "Wor"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Dri", "Pas", "Tec", "OtB"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Cro", "Fir", "Cmp", "Dec", "Vis", "Agi", "Sta"],
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}
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def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame:
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"""Read HTML file exported by FM into a Dataframe.
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Keyword arguments:
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filepath -- path to fm player html file
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"""
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player_df = pd.read_html(filepath, header=0, encoding="utf-8", keep_default_na=False)[0]
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# Clean Dataframe to get rid of unknown values and ability ranges (takes the lowest value)
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# This casts to a string to be able to split, so we have to cast back to an int later.
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player_df = player_df.replace("-", 0)
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player_df = player_df.map(lambda x: str(x).split("-")[0])
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return player_df
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def export_html_from_dataframe(player_df: pd.DataFrame, filepath: str) -> str:
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"""Export Dataframe as html with jQuery Data Tables. Taken from: https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python.
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Keyword arguments:
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filepath -- path to fm player html file
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"""
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table_html = player_df.to_html(table_id="table", index=False)
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html = f"""
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<html>
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<header>
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<link href="https://cdn.datatables.net/1.11.5/css/jquery.dataTables.min.css" rel="stylesheet">
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</header>
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<body>
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{table_html}
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<script src="https://code.jquery.com/jquery-3.6.0.slim.min.js" integrity="sha256-u7e5khyithlIdTpu22PHhENmPcRdFiHRjhAuHcs05RI=" crossorigin="anonymous"></script>
|
||||
<script type="text/javascript" src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script>
|
||||
<script>
|
||||
$(document).ready( function () {{
|
||||
$('#table').DataTable({{
|
||||
paging: false,
|
||||
order: [[12, 'desc']],
|
||||
// scrollY: 400,
|
||||
}});
|
||||
}});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
open(filepath, "w", encoding="utf-8").write(html)
|
||||
|
||||
|
||||
# TODO: Do I even want this?
|
||||
def calc_composite_scores(player_df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""Calculate Speed, Workrate and Set Piece scores.
|
||||
|
||||
Keyword arguments:
|
||||
player_df: Dataframe of Players and Attributes
|
||||
"""
|
||||
player_df["Spd"] = (player_df["Pac"] + player_df["Acc"]) / 2
|
||||
player_df["Work"] = (player_df["Wor"] + player_df["Sta"]) / 2
|
||||
player_df["SetP"] = (player_df["Jum"] + player_df["Bra"]) / 2
|
||||
return player_df
|
||||
|
||||
|
||||
def sum_attributes(player_df: pd.DataFrame, role: str, attribute_type: str, attributes: [str]) -> pd.DataFrame:
|
||||
"""Create a new Column containing the sum of provided attribute columns.
|
||||
|
||||
Keyword arguments:
|
||||
player_df: Dataframe of Players and Attributes
|
||||
role: Name of role to be used as additional column in dataframe
|
||||
attribute_type: Type of Attribute [Primary, Secondary, Tertiary]
|
||||
attributes: List of Attributes to Sum
|
||||
"""
|
||||
player_df[f"{role}_{attribute_type}"] = 0
|
||||
for attribute in attributes:
|
||||
player_df[f"{role}_{attribute_type}"] += pd.to_numeric(player_df[attribute])
|
||||
player_df[f"{role}_{attribute_type}"] = round(player_df[f"{role}_{attribute_type}"] / len(attributes), 2)
|
||||
return player_df
|
||||
|
||||
|
||||
def calc_role_scores(player_df: pd.DataFrame, role: dict) -> pd.DataFrame:
|
||||
"""Calculate Player Role scores based on selected attributes.
|
||||
|
||||
Keyword arguments:
|
||||
player_df: Dataframe of Players and Attributes
|
||||
role: Dictionary containing role name, role attributes and role attribute weightings
|
||||
"""
|
||||
player_df = sum_attributes(player_df, role["role_name"], "primary", role["primary_attributes"])
|
||||
player_df = sum_attributes(player_df, role["role_name"], "secondary", role["secondary_attributes"])
|
||||
if "tertiary_attributes" in role:
|
||||
print("here")
|
||||
player_df = sum_attributes(player_df, role["role_name"], "tertiary", role["tertiary_attributes"])
|
||||
divisor = role["primary_multiplier"] + role["secondary_multiplier"] + role["tertiary_multiplier"]
|
||||
player_df[f'{role["role_name"]}'] = round(
|
||||
(((player_df[f'{role["role_name"]}_primary'] * 5) + (player_df[f'{role["role_name"]}_secondary'] * 3) + (player_df[f'{role["role_name"]}_tertiary'] * 1)) / divisor), 2
|
||||
)
|
||||
return player_df
|
||||
|
||||
|
||||
def calc_role_scores_for_tactic_roles(player_df: pd.DataFrame, tactic_roles: [dict]):
|
||||
for role in tactic_roles:
|
||||
player_df = calc_role_scores(player_df, role)
|
||||
return player_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Parse Input args
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("-i", "--input-filepath", type=str, help="Path to Input Html file")
|
||||
parser.add_argument("-o", "--output-filepath", type=str, help="Path to Export resultant Html file")
|
||||
parser.add_argument("-r", "--roles", nargs="+", type=str, help="Space seperated list of roles for Evaluation")
|
||||
args = parser.parse_args()
|
||||
input_filepath = args.input_filepath
|
||||
output_filepath = args.output_filepath
|
||||
roles = args.roles
|
||||
|
||||
# Take Role arg and convert to list of role dictionaries
|
||||
tactic_roles = []
|
||||
for role in roles:
|
||||
tactic_roles.append(globals()[role])
|
||||
|
||||
# Inport data, calculate scores for role, export results as html
|
||||
player_df = load_html_data_to_dataframe(input_filepath)
|
||||
player_df = calc_role_scores_for_tactic_roles(player_df, tactic_roles)
|
||||
export_html_from_dataframe(player_df, output_filepath)
|
||||
Loading…
x
Reference in New Issue
Block a user