![]() The most obvious one is scoring Touchdowns ( team_score and away_team_score). Of course, teams with similar stats can still be different on some aspect not captured in the data, so we keep that in mind. The match statistics that are made available by FUMBBL are all related to important events during Blood Bowl gameplay. # "away_rush" "away_block" "away_foul" "away_cas"Ī quick summary of the FUMMBL match statistics # "home_comp" "home_pass" "home_rush" "home_block" # "race_name" "race_type" "team_score" "away_team_score" Select(-(home_cas_bh:home_cas_rip), -(away_cas_bh:away_cas_rip)) Select(race_name, race_type, team_score, away_team_score:away_cas_rip) %>% To do so, I wrote a function filter_division() that takes the source data and selects only matches from a particular division: divisions % This allows us to write a function and have this function work in parallel on a list of objects, and have it return the results also in list form.Īs this was new to me, I decided to adapt his code to process the four divisions simultaneously. In his post, he makes heavy use of functional programming using R’s purrr package. )Ī blog post from Schlice ( Schlice 2018) got me interested in BB team classification using data. ![]() (I performed the analysis for the older divisions using the 2016 ruleset as well, the plots can be found at the end of this blog post. This blog post focusses on the Blood Bowl 2020 ruleset, for this we need the “Competitive” division from FUMBBL. We start with reading in the scraped FUMBBL match data, see my previous blog posts mentioned above for details. In the next section we’ll discuss the various match statistics available from FUMBBL, but first we need to prep the data. It will be interesting to compare the patterns in the data with this categorization. Main source for this scheme is ( Dode 2017), but the bash/dash/hybrid/stunty categorization is widespread, for example at ( Breidr 2015), ( Amiral 2017) and ( Schlice 2018).
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