00:01:35 «Менеджер должен донести проблему для игроков» Напомнило «Нашу Рашу»: «Уроды! Козлы! Безногие! Всех порешу! Косоногие! Кто ж так играет!». И всё всем понятно... см.сами: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE--L2RnETHlT8.html
At 27:30, the heatmap for shots has a 'cooler' band along the very top of the penalty area. Is this caused by some kind of data error/edge-case, or do players really avoid shooting from on the painted line?!
Hi David, I am a full-time student of Statistics. In my spare time, I read your book "Soccermatics" it is absolutely amazing and inspiring to become a Soccer analyst. Could you please upload a full playlist on "Soccer analysis using R" or "How to become a Soccer analyst", also, a course or a playlist on the required concepts of Mathematics and Statistics to become a sports analyst using R programming language.?
😮 This one is quite different from the on Wikipedia. Sir, Did you create this yourself? As a student of statistics, I am currently reading your book "Soccermatics" during my spare time. It is amazing 😍. Regards: Sanaullah Khan, from Pakistan.
Hi Sir ! I've learnt a lot from you this past year , In my country there is not such any type of education of Football Analytics in my country which is India. So, your content have been a great source for me to learn about this field and not only I'm learning , I'm also putting out my work Indian Football Fanatics to make them aware about this field using my knowledge. I'm one of the fewest who is doing this on youtube. Just want to say thanks for your time and efforts you have put to aware more people !!
Thanks so much for this video and the explanation, a lot of the visualizations wouldn't make full sense without the context and this video was a great provider of that too.
Hello David, thank you for the informative videos. Do you by chance have a dataset of expected threat to work with? I can't find xThreat data anywhere online. Thanks
this models do not account for the randomness of human behaviour. as a player/team becomes more well drilled and skilled they will begin to create deviation in these models and de. how about assigning different points/weights for each player to account for these randomness.
So I might be off by a mile, but are top teams and our dear Prof. Sumpter feeding this to ML to get models and results? Or is this more of a simulation of current data?
This is quite remarkable. I was aware that pro teams have data analyst teams and models that capture movement. But also positional effect? And, with domain knowledge, also optimal tactics? This is breathtaking on my first sight.
I am very sorry if I am coming off as too forward. I read your literature for The Athletic, and I am super excited to explore this further. Please let me know if we could connect. I promise I won't take much of your time, I promise.
Greetings Professor. Now that I have finished all your videos, allow me to request if we could perhaps discuss modelling threat evaluation. I am privileged to learn so much from you, and I have an idea I have been writing on for a bit. I would be happy to keep it on the RU-vid comments, but is there some perhaps email we could connect on? May I leave my email where we could connect?
Firstly, thank you for your efforts. But after trying to make the only forward passes network, l couldn't do it, and I don't know where to get help after searching for a while. It would be nice if you can help me with that. Thank you.
I used lambda to try and find whether a pass was made forwards or not. I took the original dataframe (df) and created a new column based on the following condition: df['pass_direction'] = df[['x', 'end_x']].apply(lambda i: 'forward' if i['end_x'] > i['x'] else 'backward', axis=1) I am making an assumption that if the 'end_x' is greater than the 'x' position for that particular id then it must be a forward pass? Let me know what you think
@@aashiksingh7704 Thank you for your respond, I think that is really a good idea, I already knew the suedo code but I'm not comfortable with data science and python, I appreciate your help, thank you. 😀
@@mohamedkhaled4888 Hopefully you completed the challenge - I changed the code defining the mask. I made the same assumption as Aashik about a foward pass (end_x > x) and this seems to be correct. My solution, I added the last & and the condition following it: mask_england_fwd = (df.type_name == 'Pass') & (df.team_name == "England Women's") & (df.index < sub) & (df.outcome_name.isnull()) & (df.sub_type_name != "Throw-in") & (df.end_x > df.x)
