Australian Open 2026 Dataset

By shrijeetverma13 · February 22, 2026

Data source:

Based on the analysis of 239 matches, the data shows that 0% went to a deciding set and 0% were straight sets wins. All 239 matches (100%) fall into an 'other' category, which suggests the dataset may not have the expected deciding set or straight sets indicators populated, or uses a different classification system for match outcomes.

I've created a detailed table showing how the average number of sets played varies across different tournament rounds, from early stages through to the finals.

The analysis successfully identified the top players by matches won and calculated their average games won per match. A visualization was created showing the top 10 players, with a color scale indicating their average games won. The data tables provide detailed statistics for each player including their total match wins and average games won across all their matches.

The analysis generated three detailed data tables examining upset patterns in the tournament. These tables break down which seeded players experienced upsets and identify the rounds where upsets were most common.

Seeded players demonstrated strong performance against unseeded players across tournament rounds, winning 109 out of 138 matches (79% win rate). The visualization shows the distribution of wins by round, with seeded players consistently outperforming unseeded opponents throughout the tournament.

The analysis identified the players who achieved the most dominant straight sets victories throughout the tournament. Three data tables were generated showing the rankings of players by their straight sets wins, along with detailed dominance metrics including average games won versus lost per match.

I've created a visualization showing the average games margin (winner's games minus loser's games) across different tournament rounds. The chart displays how competitively close or one-sided matches tend to be at each stage of the tournament.

The analysis found 22 matches that went to the maximum 5 sets at the Australian Open 2026. A visualization was created showing all these marathon matches, displaying the total games played in each match, organized by tournament round, with the final scores labeled on the chart.

The analysis successfully examined upset proportions across different tournament stages. A visualization was created showing the upset rate (percentage of matches that were upsets) for each stage of the tournament.

I've generated data tables that show the relationship between seed differences and match outcomes in your dataset. The tables display how matches are distributed across different seed difference values, providing a foundation for understanding upset patterns.

The tournament spanned 18 days with a total of 239 matches. Match volume peaked on January 13, 2026, with 32 matches played that day. The visualization shows how daily match counts varied throughout the tournament, with a clear pattern of increasing volume as the event progressed.

The analysis identified the most consistent players based on how steadily they won games across multiple matches. A visualization was created showing the top 10 most consistent players, with consistency measured by standard deviation (lower values indicate more consistent performance).

I've analyzed the country head-to-head matchups in the dataset and generated tables showing the most frequent international rivalries and which countries dominated those matchups.

The analysis generated four data tables examining Round 1 performance metrics and their relationship to tournament success. These tables contain player-level statistics from first-round matches that can be used to identify patterns in how early performance correlates with advancement through later rounds.