KicktippAi experiment analysis

match-predictions/bundesliga-2025-26/pes-squad/repeated-match/md26-vfb-stuttgart-vs-rb-leipzig/repeat-25

Task: repeated-match Primary metric: avg_kicktipp_points Runs: 2 Pairings: 25

At a glance

not significant ยท p-value 0.7630
Match to predict

VfB Stuttgart vs RB Leipzig

Matchday 262026-03-15T19:30:00 UTC+01 (+01)
Actual outcome VfB Stuttgart 1 - 0 RB Leipzig
Compact head to head

Not significant

p-value 0.7630
o3
2.2800 avg points
gpt-5.5
2.1600 avg points

Prediction distribution

o3 n=25
2:1 19
1:2 4
2:2 2
gpt-5.5 n=25
2:1 18
2:2 7

Summary

Datasetmatch-predictions/bundesliga-2025-26/pes-squad/repeated-match/md26-vfb-stuttgart-vs-rb-leipzig/repeat-25
Task typerepeated-match
Primary metricavg_kicktipp_points
Alpha0.0500

Paired Wilcoxon signed-rank test on per-item Kicktipp-point differences; bootstrap confidence intervals summarize mean and median paired differences.

Dataset metadata

Stuttgart's 1-0 Matchday 26 win over Leipzig was a close top-four clash where Stuttgart leapfrogged Leipzig.

Field Value
FixtureVfB Stuttgart vs RB Leipzig
Actual ResultVfB Stuttgart 1 - 0 RB Leipzig
Matchday26
Repetitions25
Why InterestingStuttgart's 1-0 Matchday 26 win over Leipzig was a close top-four clash where Stuttgart leapfrogged Leipzig.
Competitionbundesliga-2025-26
Communitypes-squad
Season2025/2026
Slicerepeat-25
Source Poolmd26-vfb-stuttgart-vs-rb-leipzig
Sample Size25
Sample Methodrepeated-match
Scoperepeated-match
Slice Kindrepeated-match
Source Datasetmatch-predictions/bundesliga-2025-26/pes-squad

Run ranking

Rank Run Model Primary metric
1o3o32.2800
2gpt-5.5gpt-5.52.1600

Two-run comparison

not significant
Better runo3
Other rungpt-5.5
avg_kicktipp_points delta0.1200
Wilcoxon p-value0.7630
Mean difference0.1200
Median difference0.0000
Per-item W/T/L6/14/5

Effect size confidence intervals

Statistic Point estimate Low High
Mean difference0.1200-0.60000.8400
Median difference0.00000.00000.0000

Per-item win/tie/loss counts compare paired Kicktipp points for the listed run ordering on each prepared dataset item.