Visualizing Blood Bowl Gnome roster development during League play

Apr 29, 2025 4 min read Data science, Blood Bowl

This blog post uses the fumbbl_replays Python package. To install the fumbbl_replays python package, follow the instructions at the fumbbl_replays Github repository.

This analysis focuses on team development in League play. Last season I played Gnomes in our local league (B.I.G. league in Hilversum, Netherlands), and was curious how to best develop the team.

So I did some data analysis. On FUMBBL, a “meta competition” is organized called Black Box Trophy (BBT). In the Black Box Trophy, teams start off with 1M gold, and play 15 matches against random opponents in the Black box division. It uses the rule book team development rules (earning SPP, leveling up players etc). When the meta-event ends (currently after 4 months), the best performing coaches are declared winner.

This appears to be a high stakes competitive BB League environment, perfect to learn what choices pro players make to optimally develop their teams.

I decided to look at the top performing Gnome teams from the last three BBT seasons.

For this analysis, the starting point is the team_id. This is part of the url of the team on FUMBBL: https://fumbbl.com/p/team?id=1177218

These teams and their coaches stood out based on their win rates:

I wrote a function fetch_team_development_data(team_id) to fetch the rosters from the first 15 replays for each team. We can then plot this data to track the team development using the function make_team_development_plot().

import fumbbl_replays as fb

team_ids = [1177218, 1175080, 1193897, 1211512, 1210291]

for team_id in team_ids:
    res = fb.fetch_team_development_data(team_id)
    fb.make_team_development_plot(res)
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What can we learn from how these Gnome teams developed?

What starting roster is used? How are the SPP spent? What player builds do we see?

Starting roster

The Gnome roster including skill access and player cost can be found at the Blood Bowl Tactics website. As a starting roster, all five rosters start with 6 linemen and all positionals, for 14 players. Variation is in number of rerolls (Three or four), and having an Apothecary (yes or no).

Foxes

Foxes are the stars of the team, collecting SPP by scoring TDs. +MA is by far the most popular. Sure feet is also an option, to save on rerolls for the GFIs.

Treemen

Treemen skill up quickly by all those 3-die block casualties and the occassional superb TTM. Guard is favorite. Less common options are +MA, Pro and Defensive. Candlejack managed to get a second skill on both after 12 and 14 games, +MA and Block.

Defensive was a surprise for me. It turns off Guard during the opponents turn. It makes it more difficult for opponents to bring in enough assists to knock down the tree. This is strong when the trees form part of the cage formation, in combination with Beastmasters closeby that come with Guard.

Linemen

For linemen, we see mostly saving up (MVP, TTM TDs?) for Dodge. There is one lineman with a second skill, diving tackle, to serve as a marking player. Overall, it appears difficult to skill up linemen, since they do not live long enough. Of the 6 linemen in the starting roster, without Dodge only 1 or 2 survive after 15 games.

Illusionists

Same as the line gnomes, lifespan issues. After they gain Dodge protection, Leader for an extra reroll. Coach Elyod randomed pass skills, possibly in search of a cheap leader skill.

Beastmasters

Beastmasters have higher AV than the rest of the gnomes, but are more often where the action is, using their guard to assist in blocks, or protecting the ball carrier. So as with the other gnomes, they want Dodge first for more protection. After that Sidestep.

I had great fun painting up and playing Gnomes, so give em a try!

Want more BB Gnome stuff? check out the Gnome Counties Discord server. Enjoy the company of over one hundred Gnome coaches discussing and sharing all things Gnomey!

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Gertjan Verhoeven
Data Scientist / Policy Advisor

Gertjan Verhoeven is a research scientist currently at the Dutch Healthcare Authority, working on health policy and statistical methods. Follow me on Twitter or Mastodon to receive updates on new blog posts. Statistics posts using R are featured on R-Bloggers.

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