Build a Standings Table from a Season of Game Results

The standings are an output, not an input. Underneath every win-loss table is a long, plain log of individual games — who played whom and who scored what — and turning that log into a standings table is one of the most common things you'll do with sports data. Take a full season of game results and you can collapse it into wins, losses, and win percentage with a single groupby. But there's a small reshape in front of that groupby that most beginners miss — every game row touches two teams — and once you see how to handle it you'll reach for the move everywhere.

This builds on Group, Pivot, Reshape. The data is the bundled nba_home_results.csv — a real season of game results (date, home/away team, and each side's points), retrieved from Basketball-Reference — so it runs offline.

1. Look at the game log

   Each row is one game and touches two teams — one home, one away. That's the wrinkle: to count a team's results we need to see it whether it was home or away.

   import pandas as pd
   
   games = pd.read_csv("nba_home_results.csv")
   # columns: date, away_team, away_pts, home_team, home_pts

   If we only ever looked at the home_team column we'd capture half of each team's season and miss the other half. So first we reshape.

2. One row per team, per game

   We build the log twice — once from the home team's point of view, once from the away team's — renaming each side's points to neutral pf (points for) and pa (points against). Stack them and every team now has one row for every game it played.

   home = games.rename(columns={"home_team": "team", "home_pts": "pf", "away_pts": "pa"})[["team", "pf", "pa"]]
   away = games.rename(columns={"away_team": "team", "away_pts": "pf", "home_pts": "pa"})[["team", "pf", "pa"]]
   long = pd.concat([home, away], ignore_index=True)
   long["win"] = (long["pf"] > long["pa"]).astype(int)

   The win column is a boolean (pf > pa) turned into 1 or 0 with .astype(int) — which makes the next step trivial, because summing a column of 1s and 0s just counts the wins.

3. One groupby builds the table

   Group by team, sum the wins, count the games, and the standings fall out.

   standings = (long.groupby("team")
                .agg(W=("win", "sum"), G=("win", "count"))
                .reset_index())
   standings["L"] = standings["G"] - standings["W"]
   standings["WinPct"] = (standings["W"] / standings["G"]).round(3)
   standings = standings.sort_values("W", ascending=False)
   print(standings[["team", "W", "L", "WinPct"]].head(8).to_string())

   Standings, rebuilt from the game log

   Reconstructed standings from 1231 games:
                         team   W   L  WinPct
   1           Boston Celtics  64  18   0.780
   7           Denver Nuggets  57  25   0.695
   20   Oklahoma City Thunder  57  25   0.695
   17  Minnesota Timberwolves  56  26   0.683
   12    Los Angeles Clippers  51  31   0.622
   6         Dallas Mavericks  50  32   0.610
   19         New York Knicks  50  32   0.610
   16         Milwaukee Bucks  49  33   0.598

   From a flat list of more than a thousand games, two lines produced the league table — the Celtics on top at 64–18 (.780), with Denver and Oklahoma City tied behind them. No game was counted twice and none was missed, because the home-and-away reshape gave every team a complete season.

4. Chart the standings

   import matplotlib.pyplot as plt
   
   ordered = standings.sort_values("W")   # ascending so the best record lands on top
   fig, ax = plt.subplots(figsize=(8, 9))
   ax.barh(ordered["team"], ordered["W"])
   ax.set_xlabel("wins")
   fig.savefig("wins_bar.png", dpi=144, bbox_inches="tight")

   

   The same standings, now ranked at a glance. Everything in this chart was computed from raw scores — we never needed a pre-made standings table at all.

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