This is an open source project that will help people access all the important statistics for fantasy football. I want to create crawlers for both team and player statistics, and package them into neat dataframes.
Fantasy football is a game played by many Americans, however most are uneducated when it comes to the various in-depth statistics that are needed to "level up" your team, and potentially win big. Most websites gloss over important statistics, or don't include all of the information needed (think FantasyPros), or perhaps include too much information (think pro-football-reference). I want to build a one-stop-shop for these important statistics.
The Fantasy-Football Crawler library can now accept specified user inputs for the top players, and sort through specific players based on specific data. Before, the library would use getQBData(), getRBData(), and getWRData() to simply return DataFrames ranked and sorted based on factors that I personally thought were the most relevant. However, now, users can use getQBData(metrics), getRushData(metrics), and getRecMetrics(metrics) to sort QBs, receivers, and runners based on specified parameters (ex. Yds, Att), and receiving and rushing are both not limited to one position, but include all rushers and receivers across the league (ex. Rushing Data can include QBs, RBs, and WRs even), which finally allows us to incorporate players like Travis Kelce into the receiving data.
Further, an aggregate scoring method was also implemented, where users can specify a position to get a DataFrame that holds the top positional players in terms of Rushing Touchdowns, Receiving Touchdowns, and Total Touchdowns, which can be very helpful in terms of fantasy.
This project is a pure python project using modern tooling. It uses a Makefile as a command registry, with the following commands:
make: list available commandsmake develop: install and build this library and its dependencies usingpipmake build: build the library usingsetuptoolsmake lint: perform static analysis of this library withflake8andblackmake format: autoformat this library usingblackmake annotate: run type checking usingmypymake test: run automated tests withpytestmake coverage: run automated tests withpytestand collect coverage informationmake dist: package library for distribution
pip install fantasyfootball-crawler
Use the library by calling the functions that have been written in ff_functions, after importing them.
Users can call getQBData(metrics) with specified throwing metrics, to return a DataFrame that sorts based on the throwing metrics.
metrics: An array of strings that specify specific metrics to sort players by. Can be any combination of these strings: Player Age QBR Cmp% Yds TD Int
from fantasyfootball-crawler import ff_functions as ffc
metrics = ["TD", "Cmp%"]
print(ffc.getQBData(metrics))Output:
Player Age QBR Cmp% Yds TD Int
0 Patrick Mahomes*+ 27 77.6 67.1 5250 41 12
4 Joe Burrow* 26 58.7 68.3 4475 35 12
6 Josh Allen* 26 71.4 63.3 4283 35 14
7 Geno Smith* 32 60.8 69.8 4282 30 11
3 Kirk Cousins* 34 49.9 65.9 4547 29 14Users can call getRushData(metrics) with specified rushing metrics, to return a DataFrame that sorts based on the rushing metrics.
metrics: An array of strings that specify specific metrics to sort players by. Can be any combination of these strings: Player Tm Age Pos G GS Att Yds TD 1D Lng Y/A Y/G Fmb
from fantasyfootball-crawler import ff_functions as ffc
metrics = ["Yds", "Att"]
print(ffc.getRushData(metrics))Output:
Player Tm Age Pos G GS Att Yds TD 1D Lng Y/A Y/G Fmb
1 Josh Jacobs*+ LVR 24 RB 17 17 340 1653 12 93 86 4.9 97.2 3
0 Derrick Henry * TEN 28 RB 16 16 349 1538 13 65 56 4.4 96.1 6
2 Nick Chubb* CLE 27 RB 17 17 302 1525 12 69 41 5.0 89.7 1
3 Saquon Barkley* NYG 25 RB 16 16 295 1312 10 62 68 4.4 82.0 1
7 Miles Sanders* PHI 25 RB 17 15 259 1269 11 62 40 4.9 74.6 2Users can call getRecData(metrics) with specified receiving metrics, to return a DataFrame that sorts based on the receiving metrics.
metrics: An array of strings that specify specific metrics to sort players by. Can be any combination of these strings: Player Age Pos Tgt Rec Yds TD
from fantasyfootball-crawler import ff_functions as ffc
metrics = ["TD", "Yds"]
print(ffc.getRecData(metrics))Output:
Player Age Pos Tgt Rec Yds TD
7 Davante Adams*+ 30 WR 180 100 1516 14
11 A.J. Brown* 25 WR 145 88 1496 11
2 Stefon Diggs* 29 WR 154 108 1429 11
4 CeeDee Lamb* 23 WR 156 107 1359 9
21 Amari Cooper 28 WR 132 78 1160 9Users can call getScoringData(pos) with a specified position, to see the top touchdown scorers at each position: QB, RB, WR, and K (most K don't score TD's though)
pos: A string that indicates the position specified, can be "QB", "RB", "WR", or "K"
from fantasyfootball-crawler import ff_functions as ffc
print(ffc.getScoringData("QB"))Output:
Player RshTD RecTD TotalTD
33 Jalen Hurts* 13 0 13
67 Justin Fields 8 0 8
70 Josh Allen* 7 0 7
78 Daniel Jones 7 0 7
112 Joe Burrow* 5 0 5Users can call weightedRankings(df, weights) with a specified DF, and sort them with the corresponding weights. For most people, they can call another function, like getQBData() for example, to get a DataFrame, and then input their desired weights.
'df': A DataFrame with the statistics and corresponding players 'weights': An integer array that is the same size as the number of columns in df
df = getQBData(["TD"])
print(weightedRankings(df, [0.1, 0.4, 0.1, 0.3, 0.1 , 0.1, 0.1]))Output:
Player Age QBR Cmp% Yds TD Int weighted
0 Patrick Mahomes*+ 27 77.6 67.1 5250 41 12 568.99
8 Trevor Lawrence* 23 54.5 66.3 4113 25 8 516.09
7 Geno Smith* 32 60.8 69.8 4282 30 11 513.28
4 Joe Burrow* 26 58.7 68.3 4475 35 12 497.36
1 Justin Herbert 24 58.2 68.2 4739 25 10 488.96