First League · Bulgaria · Regular Season - 1

Botev Plovdiv–Lokomotiv Sofia

1–1

HT 0–0

Kick-off: Tuesday, 21 July 2026 at 18:30
Status: Match Finished
Venue: Stadion Hristo Botev, Plovdiv

What the model said, and what happened

Ended level — we gave the draw 28%double chance: correct

This verdict goes into the public record unchanged; we do not filter out the ones we get wrong. The model's track record

Goals and red cards

49'Lokomotiv SofiaD. Kostadinov (assist: Angel Lyaskov)
79'Botev PlovdivC. Meotti (assist: S. Kalu)

What we expected, and what happened

Expected goals before kick-off: Botev Plovdiv 1.46 · Lokomotiv Sofia 1.10. On the pitch: 1-1.

Referee

D. Dimitrov→

4.12

yellows per match

0.09

reds per match

matches of data

Model prediction

Why this prediction?

The model puts the most likely outcome at 44.9%: “Botev Plovdiv to win”. Second comes “a draw” at 28.1%.
Behind that distribution are the expected goals: 1.46 for Botev Plovdiv, 1.10 for Lokomotiv Sofia.
The highest double chance value is 73.0% (1X — Botev Plovdiv win or draw).
These values were computed from 1,019 past matches.

This is a prediction, not a guarantee; a probability is not a certainty.

These sentences are built by a fixed template; every number in them comes straight from the model's computation, none invented while the text was written.

Botev Plovdiv to win44.9%

Draw28.1%

Lokomotiv Sofia to win27.0%

When the model has said 40–45% in the past, 2,357 of 5,300 predictions were correct (44.5%). Accuracy record →

First half

Actual: 0–0

Botev Plovdiv lead at half-time30.1%

Level at half-time45.8%

Lokomotiv Sofia lead at half-time24.0%

First-half double chance

1X — Botev Plovdiv ahead or level76.0%

12 — not level at half-time54.2%

X2 — level or Lokomotiv Sofia ahead69.9%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1019 matches. Its edge over the base rate is markedly smaller than the full-match model's — the measured figure is on the accuracy record. Read them more cautiously. First-half goal markets are not published because they failed to beat the base rate.

Expected fouls and corners

Fouls

Botev Plovdiv: at least 9
Lokomotiv Sofia: at least 11

Corners

Botev Plovdiv: at least 4
Lokomotiv Sofia: at least 3

These figures are set to hold in at least 60% of matches; in the rest the count comes in below them. No upper bound is stated — we make no "at most" claim. Yellow cards were measured and are not published: there our model did not beat the historical average.

What these probabilities rest on

Expected goals comes out of a team's attack weighed against its opponent's defence. A strong attack is damped when it meets a strong defence — which is why comparing the ratings on their own is misleading.

How many goals Botev Plovdiv score1.46

Attack: Botev Plovdiv score close to the league average.

Opponent's defence: Lokomotiv Sofia concede 19% more than the league average, which pushes the expectation up.

Result: These two effects give an expectation of 1.46 goals (including home advantage).

How many goals Lokomotiv Sofia score1.10

Attack: Lokomotiv Sofia score close to the league average.

Opponent's defence: Botev Plovdiv's defence is close to the league average (1.09×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.10 goals (including the away penalty).

Most likely scorelines

1–114.3%1–010.5%2–19.9%

A single scoreline is always less likely than an outcome: “home win” is the sum of 1-0, 2-0, 2-1 and dozens more. So the most likely single score can be a draw while the most likely outcome is a home win; the two do not contradict each other.

Week 1 of the season. This prediction was computed from previous seasons. Summer transfers, budget changes and squad rebuilds aren't in the ratings yet — the model can only see that a team has been rebuilt once new results come in.

We measured this and we're not hiding it: in the opening week of past seasons the model came in 3.3 points below the accuracy it claimed (n=220) — for the rest of the season it beats its own claim. The tendency points this way, though it is not proof on its own: in the opening week it slightly over-calls home wins (said 42.7%, got 39.5%) and slightly under-calls draws (said 26.1%, got 28.2%).

This sample is small and not statistically established, which is why we do not correct the model for it. The correction we tried (pulling probabilities toward the league average) didn't win on holdout, so it wasn't added. Read opening-week predictions with a little more caution.