GIGIGironaGirona last 1, most recent first: won 5-2 at home to Las Palmas.W v Kick-off16:00Sun 27 SeptALALAlbaceteAlbacete last 1, most recent first: lost 0-1 at home to Oviedo.L

Not tips and not prices. Each row is the model's expected count for one player in this match — how many fouls, tackles or shots it thinks he records — plus the full spread of outcomes around it. Nothing in the projections has been compared to a bookmaker; that comparison is what the Value tab is.

1. Step 1

   His own record

   Every completed match in the dataset, as a rate per 90 minutes. The n column is how many matches that rests on.

2. Step 2

   Pulled toward his position

   A thin record gets pulled hard toward other players in his position, in his league — a goalkeeper toward goalkeepers, not toward the outfielders around him. How hard is fitted per market, not chosen: tackles pull about four times harder than shots on target.

3. Step 3

   This fixture's conditions

   How much of the stat the opponent concedes, what the referee’s matches look like, and home or away. This is where a projection stops being a season average.

4. Step 4

   Scaled to expected minutes

   A rate becomes a count only once you say how long he plays. 60 minutes carries two-thirds the exposure of 90, and the distribution is built at that exposure.

How to read each column

Model /90

The model’s rate for this match with the minutes taken back out — steps 1 to 3 already applied. It moves with the opponent and the referee, so it is not his raw career rate; the builder shows career and model rates side by side. Open any row to see it.

Exp. mins

Minutes the model expects. Confirmed means the lineup is out; provisional means it is guessing from his start rate, and is the widest of the three.

Projected

The expected count in this match, at those minutes. This is the number a line is set against.

Over 1.5, etc.

Read straight off the distribution beside it, not off how often he has beaten that line before. It answers what the model thinks, not what has happened.

n

Matches behind his own rate. Under 30 it turns amber: the projection is mostly his position group speaking, and §6.3 will not publish an edge on it.

Distribution

The whole spread, not just the average. The dashed amber cut is the line; jade bars beat it. An expected 1.8 fouls made of 2 every week and an expected 1.8 made of a 0 and a 5 are different bets, and this is where you see which one you have.

Counts are modelled as negative binomial rather than Poisson, because fouls and tackles are overdispersed and a Poisson would systematically underprice the tails — which is exactly where over bets live. Every market on this page cleared its calibration gate before it was allowed to produce a projection: when the model says 60%, it lands within a point or so of 60% out of sample.

The full write-up — every gate, every fitted parameter and what the model refuses to price — is on the methodology page.