My 2025/26 Prediction Tracker: The Data on How Good (or Bad) My Scoreline Calls Are
Leagues covered: Championship & League One | Season: 2025/26
Total predictions made: 137 | Results in: 135 | Points earned: 89 / 405 possible
Article Date: 9 March 2026 | Gameweeks covered: GW28–GW36
Every week I put out scoreline predictions for Championship and League One fixtures. Not just a result call — an actual scoreline. Three points for the exact score, one point for the correct result, zero for a miss. It’s a brutal system. This post is the full data audit of how those predictions are holding up across the season so far.
I’ve been tracking predictions since GW28 in League One and GW30 in the Championship. With 135 settled results across 12 gameweeks, there’s finally enough data to see patterns — which gameweeks I nail, which results I systematically get wrong, and which teams I simply cannot call.
Scoring system: 3pts = correct scoreline | 1pt = correct result (wrong score) | 0pts = wrong result. Max possible per game = 3.
The Headline Numbers
137
Predictions Made
89
Total Points
22%
of Max Possible Pts
0.66
Avg Pts per Game
Championship
| GWs covered | 30, 31, 32, 33, 35, 36 |
| Predictions | 71 |
| Played | 69 |
| Points earned | 46 / 207 |
| Hit rate | 22.2% |
| Best GW | GW32 — 13 pts |
| Worst GW | GW30 — 4 pts |
League One
| GWs covered | 28, 31, 32, 34, 35, 36 |
| Predictions | 66 |
| Played | 66 |
| Points earned | 43 / 198 |
| Hit rate | 21.7% |
| Best GW | GW34 — 11 pts |
| Worst GW | GW32 — 3 pts |
Overall Stats by League
Prediction Accuracy (2025)
Championship
| Predictions played | 544 |
| Correct outcomes | 234 |
| Accuracy | 43% |
League One
| Predictions played | 545 |
| Correct outcomes | 247 |
| Accuracy | 45.3% |
Women's Super League
| Predictions played | 26 |
| Correct outcomes | 16 |
| Accuracy | 61.5% |
Gameweek by Gameweek
The bar chart below shows my points haul each gameweek — green = strong week, yellow = average, red = rough. The line overlay tracks my cumulative total, which is the number that really matters.
Championship — Points by Gameweek
Championship GW Points + Cumulative Trend
Correct Outcomes by Matchday (2025)
13 pts from 12 games
9 pts each
4 pts from 12 games
League One — Points by Gameweek
League One GW Points + Cumulative Trend
Correct Outcomes by Matchday (2025)
11 pts from 12 games
8 pts from 10 games
3 pts from 8 games
How I Predict: Home Win, Draw, Away Win
Do I predict too many home wins and not enough draws? This chart breaks down whether the outcome type I predicted translated into points — useful for spotting systematic biases in how I call games.
Championship
Correct Score / Correct Result / Wrong — by Predicted Outcome
Prediction Breakdown (2025)
League One
Correct Score / Correct Result / Wrong — by Predicted Outcome
Prediction Breakdown (2025)
Reading the chart: Green = exact score nailed (3pts). Blue = right result, wrong score (1pt). Red = wrong result entirely (0pts). Hover for percentages.
Draw predictions are my weakest category by some distance — I don’t predict them often enough, and when I do, the miss rate is noticeably higher than for home or away wins. That’s a bias I need to address: when the data points to a tight game, I keep defaulting to a narrow win rather than backing the stalemate.
Which Teams Am I Best (and Worst) at Predicting?
Every team appears in these charts twice per GW — once as the home side and once as the away side. Teams at the top of the chart are ones where my predictions consistently earn points. Teams at the bottom are a blind spot.
Championship — Average Points per Prediction by Team
Championship: Avg Pts per Prediction When This Team Is Involved (min. 2 predictions)
Accuracy by Team (2025)
League One — Average Points per Prediction by Team
League One: Avg Pts per Prediction When This Team Is Involved (min. 2 predictions)
Accuracy by Team (2025)
Teams I Read Well
West Brom are my standout — no other Championship team comes close in terms of avg points earned when they’re involved. Sheffield United and Preston also sit near the top; both have a recognisable shape that seems to translate well into scoreline calls. In League One, Doncaster and Lincoln are the two teams I’ve read most accurately, both averaging above 1.0 pts per prediction.
Teams That Confound Me
Leicester and Middlesbrough are the Championship games I simply cannot call — both sit at the foot of the chart. In League One, Peterborough and Stockport County are the problem teams; both feel predictable on paper but keep producing results I don’t see coming. These are the fixtures I probably need to approach with more caution.
My Favourite Scorelines — And How Often They Land
Every predictor has their go-to scorelines. The chart below shows which scorelines I’ve predicted most often this season and how they’ve performed — stacked green (exact hit) / blue (right result) / red (miss).
Championship — Most-Predicted Scorelines
Championship: Top Predicted Scorelines (hover for exact score hit rate)
This view is retired. The current model predicts outcome probabilities (home / draw / away) rather than exact scorelines, so a most-predicted-scoreline chart no longer applies. See the other scorecard panels for outcome accuracy.
League One — Most-Predicted Scorelines
League One: Top Predicted Scorelines (hover for exact score hit rate)
This view is retired. The current model predicts outcome probabilities (home / draw / away) rather than exact scorelines, so a most-predicted-scoreline chart no longer applies. See the other scorecard panels for outcome accuracy.
Reading the chart: Bar height = how many times I’ve predicted that scoreline. Hover on any bar to see the exact score hit rate (%). Green portion = times I nailed the exact score.
2-1 is my go-to in both leagues — it’s the instinctive call when I fancy the home side but think the away team will make a game of it. In the Championship it’s also where I’ve earned most of my correct score points, so that instinct isn’t entirely wrong. The 2-0 predictions in League One are where I’m leaking most — I keep backing clean-sheet wins that don’t materialise, and the miss rate on that scoreline is noticeably high.
The Verdict
So — am I actually any good at this?
What’s Working
- GW32 in the Championship proves I can have genuinely strong weeks — 13pts from 12 games is a real haul under this scoring system
- League One has been slightly more consistent overall, with fewer complete disaster weeks and a more even spread of points
- West Brom, Sheffield United and Preston in the Championship, and Doncaster and Lincoln in League One, are teams I’ve clearly got a decent read on — worth leaning into that
Where I’m Leaking Points
- Draw predictions are my clear weak spot — I don’t back them often enough, and when I do, the success rate is poor
- GW30 in the Championship (just 4pts from 12 games) and GW32 in League One (3pts from 8 games) were both rough — I need to understand what I misjudged those weeks
- Leicester and Middlesbrough in the Championship, and Peterborough and Stockport in League One, are consistent blind spots that are costing me points every time they appear
22% of maximum possible points sounds bleak until you contextualise it. In practice I’m averaging 0.66pts per game across 135 settled predictions — and correct scoreline calls are genuinely rare in football, where the base rate for any individual score is typically well under 20%. The bigger issue right now is variance: the gap between my best and worst gameweeks is too wide, which suggests I’m not reading fixture difficulty consistently enough yet.
Season Targets
Championship target
85 pts
by season end
League One target
80 pts
by season end
Correct score rate
8%
target hit rate
Bottom line: The data doesn’t lie — I’m not a prediction genius, but 89 points from 135 settled games means I’m not random noise either. I’ll update this tracker at GW40 and again at the end of the season to see whether the patterns hold.
