tsb — Sports Standings

1 · Build the standings table

Each match contributes two rows to the per-team view (one for each team).

TypeScript

import { DataFrame, concat, rankSeries } from "tsb";

const matches = DataFrame.fromColumns({
  home: ["Lions","Tigers","Bears","Wolves","Lions","Bears","Tigers","Wolves","Lions","Tigers","Bears","Wolves"],
  away: ["Tigers","Bears","Wolves","Lions","Wolves","Tigers","Lions","Bears","Bears","Wolves","Lions","Tigers"],
  home_goals: [2,1,3,0,4,2,1,2,1,2,3,1],
  away_goals: [1,1,2,2,0,2,3,1,1,1,2,1],
});

// Convert each match to 2 team-rows (home + away view)
const home = DataFrame.fromColumns({
  team: [...matches.col("home").values],
  gf:   [...matches.col("home_goals").values],
  ga:   [...matches.col("away_goals").values],
});
const away = DataFrame.fromColumns({
  team: [...matches.col("away").values],
  gf:   [...matches.col("away_goals").values],
  ga:   [...matches.col("home_goals").values],
});
const teamMatches = concat([home, away], { ignoreIndex: true });

// Per-row outcome (W/D/L) and points
const gf = [...teamMatches.col("gf").values].map(Number);
const ga = [...teamMatches.col("ga").values].map(Number);
const outcome: string[] = gf.map((g, i) => g > ga[i]! ? "W" : g < ga[i]! ? "L" : "D");
const pts = outcome.map(o => o === "W" ? 3 : o === "D" ? 1 : 0);
const enriched = teamMatches.assign({ outcome, pts });

const rank = rankSeries(final.col("pts"), { method: "min", ascending: false });
const withRank = final.assign({ rank });

console.log("League table:");
console.log(withRank.toString());

import pandas as pd
matches = pd.DataFrame({
    "home": ["Lions","Tigers","Bears","Wolves","Lions","Bears","Tigers","Wolves","Lions","Tigers","Bears","Wolves"],
    "away": ["Tigers","Bears","Wolves","Lions","Wolves","Tigers","Lions","Bears","Bears","Wolves","Lions","Tigers"],
    "home_goals": [2,1,3,0,4,2,1,2,1,2,3,1],
    "away_goals": [1,1,2,2,0,2,3,1,1,1,2,1],
})
home = matches.rename(columns={"home":"team","home_goals":"gf","away_goals":"ga"})[["team","gf","ga"]]
away = matches.rename(columns={"away":"team","away_goals":"gf","home_goals":"ga"})[["team","gf","ga"]]
team_matches = pd.concat([home, away], ignore_index=True)
team_matches["outcome"] = (team_matches["gf"] - team_matches["ga"]).apply(
    lambda x: "W" if x>0 else ("D" if x==0 else "L"))
team_matches["pts"] = team_matches["outcome"].map({"W":3,"D":1,"L":0})
table = team_matches.groupby("team", as_index=False).agg(
    pts=("pts","sum"), gf=("gf","sum"), ga=("ga","sum"))
table["gd"] = table["gf"] - table["ga"]
table = table.sort_values(["pts","gd"], ascending=False)
table["rank"] = table["pts"].rank(method="min", ascending=False).astype(int)
print(table)

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⚽ Sports League Standings

1 · Build the standings table