Group vs Knockout Scoring at the 2026 World Cup, with groupby

Every World Cup produces the same claim by its second week: "the knockouts tighten up." It's a perfect first analytics question because it's checkable with one groupby — and because the answer, on real data, is messier and more interesting than the cliché. We'll test it on the complete record of the 2026 World Cup: all 104 matches, group stage through the final, bundled as wc2026_results.csv so everything runs offline.

This builds on Grouping, Pivoting and Reshaping — the general tool — and applies it to a single sharp question. The rhythm here (derive columns, group, aggregate, chart with sample sizes showing) is the same one behind every stage-split, season-split or home/away-split table you've ever read.

1. Load the matches and derive the columns you wish existed

   The CSV has one row per match: date, stage, the two teams, and the two scores. The three columns the question needs — total goals, did both teams score, was it goalless — don't exist yet, so make them. This derive-then-aggregate rhythm is most of real analysis.

   import pandas as pd
   
   df = pd.read_csv("wc2026_results.csv")
   df["total_goals"] = df["home_goals"] + df["away_goals"]
   df["both_scored"] = (df["home_goals"] > 0) & (df["away_goals"] > 0)
   df["goalless"]    = df["total_goals"] == 0

   One row per real match

         date stage          home               away  total_goals
   2026-06-11 group        Mexico       South Africa            2
   2026-06-12 group   South Korea            Czechia            3
   2026-06-12 group        Canada Bosnia-Herzegovina            2
   2026-06-13 group United States           Paraguay            5
   ...
   104 matches, stages: group, round of 32, round of 16, quarterfinal, semifinal, third place, final

2. One groupby, five answers

   Group by stage and aggregate five ways at once with named aggregations: how many games, how many goals, goals per game, the share where both teams scored, and the goalless share. reindex puts the seven stages in tournament order instead of alphabetical — a tiny step that saves every reader of your table a double-take.

   stage_order = ["group", "round of 32", "round of 16", "quarterfinal",
                  "semifinal", "third place", "final"]
   by_stage = (
       df.groupby("stage")
         .agg(games=("total_goals", "size"),
              goals=("total_goals", "sum"),
              goals_per_game=("total_goals", "mean"),
              both_scored=("both_scored", "mean"),
              goalless=("goalless", "mean"))
         .reindex(stage_order)
   )

   The cliché, confronted

                 games  goals  goals_per_game  both_scored  goalless
   stage                                                            
   group            72    215            2.99        0.542     0.097
   round of 32      16     42            2.62        0.562     0.000
   round of 16       8     23            2.88        0.500     0.125
   quarterfinal      4     12            3.00        0.750     0.000
   semifinal         2      5            2.50        0.500     0.000
   third place       1     10           10.00        1.000     0.000
   final             1      1            1.00        0.000     0.000
   
   Knockouts pooled: 32 games, 2.91 goals/game (groups: 2.99)
   ...and without the third-place game: 31 games, 2.68 goals/game

   Read the goals-per-game column honestly. Scoring did dip when the knockouts began — 2.99 in the groups to 2.62 in the round of 32 — then climbed back through the round of 16 (2.88) and the quarterfinals (3.00, above the group rate). Pooled, the 32 knockout games finished at 2.91 goals per game, a whisker under the groups' 2.99 — but that pooled figure hides a ten-goal third-place game (France 4–6 England, the classic dead-rubber goal-fest). Strip that one famous outlier and the 31 competitive knockouts sit at 2.68 — a real tightening, about a third of a goal per game, but nothing like the strangled football the cliché promises. And the one genuinely tight game was the biggest: the final, Spain 1–0 Argentina.

3. Chart it, with the sample sizes on the bars

   These numbers deserve a chart with a warning label: the stages have wildly different sample sizes (72 games down to a single match), so print n on every bar. That ten-goal spike at "third place" is one game, and the bar says so on its face — a rate from one match is a scoreline, not a scoring environment. The same discipline that keeps an 8-game rate honest keeps a 1-game rate from lying to you.

   fig, ax = plt.subplots(figsize=(10.2, 5.6))
   bars = ax.bar(by_stage.index, by_stage["goals_per_game"])
   for b, (_, row) in zip(bars, by_stage.iterrows()):
       ax.annotate("n=%d" % row["games"],
                   (b.get_x() + b.get_width()/2, 0.22), ha="center")
   ax.axhline(df["total_goals"].mean(), ls="--")   # tournament average

   

4. Interrogate the cliché's strongest prediction: 0-0s

   If knockouts really strangle football, goalless games should pile up there — teams playing for penalties. Count them by stage.

   zeros = df[df["goalless"]]
   print(zeros.groupby("stage").size())

   Where the 0-0s actually live

   Goalless games: 8 of 104 (8%)
   stage
   group           7
   round of 32     0
   round of 16     1
   quarterfinal    0
   semifinal       0
   third place     0
   final           0
   
   The cliche says knockouts breed 0-0s. The full tournament: 7 in 72 group
   games, 1 in 32 knockout games - elimination football stayed DECISIVE, not cagey.

   Seven of the tournament's eight goalless games happened in the group stage. The knockouts produced exactly one 0-0 in 32 elimination games — the last round-of-16 tie, settled on penalties — and then the tournament closed with eight straight knockout games that all had goals, including that ten-goal third-place match. Over the full 2026 World Cup, elimination football was decisive, not cagey — the opposite of the cliché's strongest claim.

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