Data science, for the love of it
Will Millwall win the Championship?
Outside of work I have three passions: data science, flying, and Millwall Football Club.
Whichever club you support, thank you for stopping by. Football is one of the few things that brings people together across every background, and that is worth celebrating.
This page is where two of those passions meet. Below is a living model of Millwall’s promotion chances, built up in four steps from a simple coin flip to a full Monte Carlo simulation of the rest of the season.
It refreshes around every matchday from live Championship data, and it follows the same approach I take with clients: start simple, test every assumption, and only add complexity when it earns its place.
0.0%
chance of promotion this season (top 6)
The matchday update
After two tough away defeats, we're sitting 16th with 18 points from 15 games and that title talk is well and truly off the table at 0.0%, though the model still fancies a 0.8% playoff shot if we can turn things around. The Derby demolition showed we've got it in us, but we need to rediscover that form when Queens Park Rangers visit The Den on Saturday, come on you Lions.
Next up: home vs Queens Park Rangers FC · Saturday, 21 November
Current position
16.
of 24
Projected finish
14.
range 10–19
Projected final points
60
51–68
Last five
Recent results weighted higher through an Elo-style update, then 20,000 simulations of the rest of the season. This is the headline number.
0.0%
Win the title
0.0%
Automatic promotion
0.8%
Reach the playoffs
Each figure is the share of 20,000 simulated seasons in which that outcome happened. Promotion means top two (automatic) or top six (through the playoffs); the title means finishing first.
Where the simulation says we finish
Hover or tap a bar
14th12.6%· most likely
The table today
| Pos | Team | GD | Pts |
|---|---|---|---|
| 1 | Birmingham City | +14 | 29 |
| 2 | Middlesbrough | +11 | 29 |
| 3 | Stoke City | +6 | 29 |
| 4 | Southampton | +14 | 28 |
| 5 | Wolverhampton Wanderers | +8 | 28 |
| 6 | West Bromwich Albion | +8 | 28 |
| 7 | Swansea City | +4 | 26 |
| 8 | Queens Park Rangers | +6 | 24 |
| 9 | West Ham United | +14 | 23 |
| 10 | Portsmouth | +2 | 22 |
| 11 | Sheffield United | +2 | 22 |
| 12 | Norwich City | +8 | 21 |
| 13 | Blackburn Rovers | -1 | 21 |
| 14 | Cardiff City | 0 | 18 |
| 15 | Wrexham | -2 | 18 |
| 16 | Millwall | -4 | 18 |
| 17 | Bristol City | -8 | 18 |
| 18 | Bolton Wanderers | -9 | 17 |
| 19 | Charlton Athletic | -10 | 17 |
| 20 | Watford | -10 | 15 |
| 21 | Lincoln City | -11 | 15 |
| 22 | Burnley | -9 | 10 |
| 23 | Derby County | -16 | 10 |
| 24 | Preston North End | -17 | 8 |
Projected final table
| Pos | Team | xPts |
|---|---|---|
| 1 | Southampton | 84 |
| 2 | Birmingham City | 84 |
| 3 | Middlesbrough | 83 |
| 4 | Wolverhampton Wanderers | 79 |
| 5 | Stoke City | 79 |
| 6 | West Ham United | 77 |
| 7 | West Bromwich Albion | 77 |
| 8 | Swansea City | 76 |
| 9 | Queens Park Rangers | 72 |
| 10 | Norwich City | 71 |
| 11 | Sheffield United | 68 |
| 12 | Portsmouth | 64 |
| 13 | Blackburn Rovers | 61 |
| 14 | Millwall | 60 |
| 15 | Cardiff City | 59 |
| 16 | Wrexham | 58 |
| 17 | Bristol City | 53 |
| 18 | Bolton Wanderers | 53 |
| 19 | Charlton Athletic | 51 |
| 20 | Watford | 47 |
| 21 | Burnley | 47 |
| 22 | Lincoln City | 47 |
| 23 | Derby County | 39 |
| 24 | Preston North End | 35 |
Each team’s average points across 20,000 simulated seasons, ranked. The most likely shape of the final table, though every line carries a wide band.
How the model works
- 1
Naive prior
No knowledge at all: 24 teams, so a flat 1 in 24. The baseline every model must beat.
- 2
Team strength
Attack and defence ratings from goals scored and conceded, with home advantage, then a full season simulated from scratch. Position-blind.
- 3
+ Current standings
Keeps the points already on the board and simulates only the remaining fixtures with those strength ratings.
- 4
+ Form & simulation
Recent results weighted higher through an Elo-style update, then 20,000 simulations of the rest of the season. This is the headline number.
The data science, and why
Poisson goals model
Football goals follow a Poisson process closely enough to be useful, so each team’s scoring is a Poisson rate built from goals scored and conceded versus the league average, plus a home-advantage term. It is the workhorse of football forecasting: transparent, fast, and hard to overfit.
Shrinkage (a light prior)
Six games in, raw scoring rates are noisy. I pull each strength toward the league average with a small Bayesian-style prior, so one 5-1 does not hijack the whole forecast. It fades as real games accumulate.
Elo-style form
A season average treats August and last Saturday the same. An Elo update nudges each rating by how surprising a result was, letting recent form count for more without discarding the base rate.
Monte Carlo simulation
There is no tidy formula for “probability of finishing top six” once every remaining fixture and tie-breaker is in play. So I simulate the rest of the season 20,000 times, sampling each scoreline from the Poisson rates, and count how often Millwall land in each position. This is how FiveThirtyEight’s SPI and Opta’s supercomputer work.
Layered on purpose
Each tab adds exactly one idea, so you can watch the estimate move from a naive prior to a full simulation. A model you cannot explain is a model you should not trust, and I would rather show my working.
Honest limitations
- Goals, not expected goals. Free xG data does not exist for the Championship, so the model learns from actual goals. Goals are noisier than xG, so early-season numbers swing a lot.
- No context. It knows nothing of injuries, suspensions, transfers, new managers, fixture congestion or a derby-day mood. A key striker out until May only shows up once the results do.
- Independence assumptions. Poisson treats goals as independent; real games have state effects (2-0 up and cruising). Dixon-Coles corrects the very low scores; the rest is accepted noise.
- The table is not destiny. With most of the season to play, the bands are wide on purpose. The single number is the centre of a range, not a promise.
- It is for the love of it. A personal project to show the method honestly. Not a betting model, not affiliated with the club.
The real thing
This is my model, not the club. For the actual Lions, the fixtures, the tickets and the news, go to the source. And if you fancy it, the shop does a lovely shirt (Poling can vouch for it).
Fixtures, results and standings from football-data.org, refreshed around each matchday. Goals-based model (free expected-goals data does not exist for the Championship). A personal project, not affiliated with Millwall FC, and absolutely not betting advice.
8/11/2026 · 2026/2027 · 180 played