Not quite sure what you are asking, so going to make an assumption that these "points" apply to each player/row, then you can find similar players by looking for close scores.
To do this, simply setup a loop to calculate this out (I'd do as by vectorized column because it's faster as opposed to iterating by rows to do it.)
You may need to adjust the column names to match yours in your data set in the rules dictionary I created.
import pandas as pd
import requests
import numpy as np
# Sample Data
mlb_api = 'https://bdfed.stitch.mlbinfra.com/bdfed/stats/player'
payload = {
'stitch_env': 'prod',
'season': '2020',
'sportId': '1',
'stats': 'season',
'group': 'hitting',
'gameType': 'R',
'offset': '0',
'sortStat': 'onBasePlusSlugging',
'order': 'desc'}
jsonData = requests.get(mlb_api, params=payload).json()
df = pd.DataFrame(jsonData['stats'])
# Here is the Code you'll need
rules = {
'gamesPlayed':20,
'atBats':75,
'runs':10,
'hits':15,
'doubles':5,
'triples':4,
'homeRuns':2,
'rbi':10,
'baseOnBalls':25,
'strikeOuts':150,
'stolenBases':20,
'avg':.001,
'slg':.002}
df['Score'] = 0
for col, points in rules.items():
df['Score'] += np.floor(df[col].astype(float) / points)
df = df.sort_values('Score', ascending=False).reset_index(drop=True)
Output:
print(df[['playerName','Score']])
playerName Score
0 Juan Soto 719.0
1 Freddie Freeman 691.0
2 Marcell Ozuna 687.0
3 DJ LeMahieu 680.0
4 Jose Abreu 658.0
5 Trea Turner 657.0
6 Dominic Smith 646.0
7 Jose Ramirez 623.0
8 Nelson Cruz 623.0
9 Corey Seager 623.0
10 Manny Machado 622.0
11 Wil Myers 614.0
12 Luke Voit 610.0
13 Mike Trout 607.0
14 Mike Yastrzemski 602.0
15 Michael Conforto 600.0
16 Teoscar Hernandez 600.0
17 Eloy Jimenez 598.0
18 Mookie Betts 597.0
19 Fernando Tatis Jr. 590.0
20 Brandon Lowe 568.0
21 Ronald Acuna Jr. 562.0
22 Bryce Harper 561.0
23 George Springer 556.0
24 Anthony Rendon 552.0
