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May I ask what language you are using to scrape the BBAPI? I've been working on building something using Python to scrape the data from BBAPI but it's in the early stages of design, such as logging in, getting the basic Team Info and throwing it into a dictionary.

Before I go any further, I need to sit down and map out what I want to achieve with the data and then start planning the functions from that point forward.

Anyways, I don't want to hijack your thread and it's purpose so I'll leave that for a discussion later.

First of all thanks the author for this massive information, great work.
I have a question though. Anybody any idea how in this game PPP of my C is less than my opponents'??
http://digitalfieldtheory.com/match/101858254/boxscore.aspx
His centers went 1-5 FG 1-1 FT (Starting center 0 points in 36mins) total of 3 Pts and my Centers were 5-10FG 1-4 Ft - 11 points in total. Still, PPP for my C was 111 compared to my opponents 112,4. Very strange.

Last edited by Nicolas at 4/19/2018 1:50:30 AM

great job!

it seems base tactic is the one which provides the highet % of TS%. correct?

I'm not that advanced with coding, I do modelling in VBA for work so I used VBA (also this allowed me to debug all the problems which may happen infrequently, such as Lucky Fans getting into the action. I just used the same logic behind Moutlinho's tool which was coded in VB (I believe), but I never got that to work properly. I don't know how he solved the problem of the turnovers which seems impossible with just the pbp.

My tool simply does this: it takes the page source of the play by play and breaks down each entry. Instead of copying and pasting 1 at a time for 200 games, I get VBA code to do it for me. Using the page source rather than the text also makes it easy to identify the action as the same action can have different text in the pbp.

The BBApi is a much much faster and easier thing to download. After all this is what Buzzer-Manager also does. All the info is there in xml format and it does not need you to go to a navigate to webpage, all you need to do is parsing the information you want from the xml text.

Last edited by Lemonshine at 4/19/2018 10:27:24 AM

Yes but keep in mind a couple of things:
1) some tactics have a low sample as Teemo said
2) 'weaker' tactics like BO, R&G, outside iso etc are often played when the result is not in question, so when there is a disparity between teams and the stronger one plays an unusual tactic to prevent GDP

The number of possessions and which position is taking which kind of shot are also key to understand what exactly is going on. All the teams in the KO phase of the B3 may be more 'specialised' than your average BB team. So people playing outside tactics consistently may have built their team to be truly elite in some departments.

This analysis does not show you the baseline % for each tactic, it only shows the result of specific games. I suppose we can take some comfort that the numbers don't seem to penalise outside tactics so much, but again these numbers only cover one facet of the game, they will tell you nothing about rebounding, about turnovers, about pace and it is very likely that offensive tactics also have an impact in all of these other aspects. If we wanted to analyse the baseline it would be better to take a few D4 leagues with no bots than the cream of the crop because then you will have teams filled with a lot of average players.

Last edited by Lemonshine at 4/19/2018 10:25:28 AM

I chose this data since BBB KO games are high level games, played at 12 TS / same attitude since s34 and does not suffer from HCA influence. I think this is the best data available to analyze. Indeed GDP influence the output data, but this is also a present reality of BB : you have way more chances to be predicted inside.

This data should be compared to England first division data to be validated since the number of occurences of some tactics are low and uncertainties about them are probably high. I will try to find some interesting outside offense BBB round robin games to add to the database in order to reduce these uncertainties.

I just want to share an idea, not so sure how good/bad it is but I'm confident you and Lemonshine can :-)

do you think it could be useful taking a new set of data from games bewteen BOT teams only? It is true that there is the HCA influence, but you will have a huge set of data, and the low level of the players can give you minimal variance from the medium values.
I don't know if there are series made of BOT teams only. You can also use only data from matches with few points of difference in the final result. And these data can be a sort of "reference" for % values of interesting parameters.

there are 450-500 lines of play-by-play on average. this means 100 games = 45,000 lines, 200 games = 90,000. The more games you use the slower the code gets, until you have 800k+ lines and you really need to use a database software like SQL or Access, because in Excel it will be hard to run.

My code was always meant to be a scouting tool (with more sophisticated defensive and offensive stats than Buzzer Manager provides) or an advanced stats generator (I thought it would be cool to compile this data for the top 5 leagues in England and my league in Utopia). I did this analysis just to start the conversation.

If we wanted to investigate baseline numbers we could just get some d4 (fully human) and look at that. I'm thinking it's always better to do a whole league rather than a cup like the B3 because everyone plays everyone which should average out a lot of things, while in the B3 the best teams play more games. But yes, recent B3 games don't have the influence of Enth and HCA, which makes them valuable because of it.

yes! and that's why you can use these data as a reference for different tactics measurements