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Post by InTheKnow on Jan 1, 2024 9:56:18 GMT -5
With the addition of a 3rd coach I’ve seen more and more programs invest in a Stat Analysis person. Those guys on the bench typing away, coding the game.
Who are some of those people who do this and who are the best at it?
This seems like a new space of employment that’s growing.
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Post by statsqueen on Apr 26, 2024 13:40:28 GMT -5
Not everyone is "coding the game" the same way. Some use DataVolley. Some don't bother with it. The real magic is what happens before and after the games (predictive analytics). No one is going to share what they do if they're smart.  |
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Post by mplsgopher on Apr 27, 2024 10:35:35 GMT -5
A lot (vast majority if not all?) of teams in the major US professional sports leagues employ data scientists now.
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Post by mikegarrison on Apr 27, 2024 12:37:08 GMT -5
A lot (vast majority if not all?) of teams in the major US professional sports leagues employ data scientists now. As a professional data cruncher, I can tell you that it is one thing to employ a "data scientist", and it is another thing to get useful results from that. It depends on whether the data analyst is good, whether the data is good, whether the data is the *right* data, and also whether there is anything actionable to be found in the data. |
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Post by mplsgopher on Apr 27, 2024 13:19:06 GMT -5
A lot (vast majority if not all?) of teams in the major US professional sports leagues employ data scientists now. As a professional data cruncher, I can tell you that it is one thing to employ a "data scientist", and it is another thing to get useful results from that. It depends on whether the data analyst is good, whether the data is good, whether the data is the *right* data, and also whether there is anything actionable to be found in the data. These days black box models consisting of some kind of deep neural network are the standard. |
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Post by datanerd on Apr 28, 2024 19:00:52 GMT -5
A lot (vast majority if not all?) of teams in the major US professional sports leagues employ data scientists now. As a professional data cruncher, I can tell you that it is one thing to employ a "data scientist", and it is another thing to get useful results from that. It depends on whether the data analyst is good, whether the data is good, whether the data is the *right* data, and also whether there is anything actionable to be found in the data. It also depends on whether the coach/staff actually implement the recommendations. A lot of companies (and probably a few teams) employee data scientists, but still use "their gut" to make all decisions. |
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Post by statsqueen on Apr 30, 2024 8:20:56 GMT -5
As a professional data cruncher, I can tell you that it is one thing to employ a "data scientist", and it is another thing to get useful results from that. It depends on whether the data analyst is good, whether the data is good, whether the data is the *right* data, and also whether there is anything actionable to be found in the data. It also depends on whether the coach/staff actually implement the recommendations. A lot of companies (and probably a few teams) employee data scientists, but still use "their gut" to make all decisions. Truth. It's so hard not to bring the "I told you so", but that's unprofessional.... I will say, though, to Mike.... If you have the right data person, it's their job not to stop combing the data until they find something actionable. If you ask the right questions and you can figure out how to quantify it, anything can become data. That means you need more than a code-monkey in the position, but most programs don't want to pay for someone with those skills.... |
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bluepenquin
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Post by bluepenquin on Apr 30, 2024 8:34:50 GMT -5
With the addition of a 3rd coach I’ve seen more and more programs invest in a Stat Analysis person. Those guys on the bench typing away, coding the game. Who are some of those people who do this and who are the best at it? This seems like a new space of employment that’s growing. IDK - but a 'Stat Analysis' isn't the same thing as a guy on the bench typing away? I suspect every team is using statistical analysis. And getting a coach that understands this is essential. Curious - couldn't you just have someone else doing the stat inputting, or just have a coach dissect this after the game from film? |
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Post by statsqueen on Apr 30, 2024 9:08:58 GMT -5
With the addition of a 3rd coach I’ve seen more and more programs invest in a Stat Analysis person. Those guys on the bench typing away, coding the game. Who are some of those people who do this and who are the best at it? This seems like a new space of employment that’s growing. IDK - but a 'Stat Analysis' isn't the same thing as a guy on the bench typing away? I suspect every team is using statistical analysis. And getting a coach that understands this is essential. Curious - couldn't you just have someone else doing the stat inputting, or just have a coach dissect this after the game from film? Yes, you could, if the coach knew what to look for. I will say...in my role I'm not the game stat person. I do not "code" DataVolley during matches. I take very specific data that my coaches utilize effectively in-game. Could a grad student or a codemonkey do what I do in-game? Depends on their volleyball knowledge for the taking-down of the stats. Depends on their decision-making skills and understanding of the numbers for the "alert" to the coaching staff.... Most of the opponent scouting work is done before the match, and could honestly be a remote position. The team fixing its own issues practice after practice is the kicker, and coaches have different ranges of whether they even listen to that stuff.... Also, data scientists come in different flavors. The machine learning "black box" people might come up with good overall trends, but they aren't asking questions that have nuance, and they inherently miss anything that isn't already in a given data set.... |
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Post by volleyguy on Apr 30, 2024 9:13:52 GMT -5
IDK - but a 'Stat Analysis' isn't the same thing as a guy on the bench typing away? I suspect every team is using statistical analysis. And getting a coach that understands this is essential. Curious - couldn't you just have someone else doing the stat inputting, or just have a coach dissect this after the game from film? Yes, you could, if the coach knew what to look for. I will say...in my role I'm not the game stat person. I do not "code" DataVolley during matches. I take very specific data that my coaches utilize effectively in-game. Could a grad student or a codemonkey do what I do in-game? Depends on their volleyball knowledge for the taking-down of the stats. Depends on their decision-making skills and understanding of the numbers for the "alert" to the coaching staff.... Most of the opponent scouting work is done before the match, and could honestly be a remote position. The team fixing its own issues practice after practice is the kicker, and coaches have different ranges of whether they even listen to that stuff.... Also, data scientists come in different flavors. The machine learning "black box" people might come up with good overall trends, but they aren't asking questions that have nuance, and they inherently miss anything that isn't already in a given data set.... Yes, but the question is, do they know anything about volleyball? |
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Post by mikegarrison on Apr 30, 2024 12:43:08 GMT -5
I will say, though, to Mike.... If you have the right data person, it's their job not to stop combing the data until they find something actionable. If you ask the right questions and you can figure out how to quantify it, anything can become data. I have spent my entire career working with highly chaotic data (noise and combustion and turbulent flow). If you have enough randomness, you can find patterns in it, but it is not at all guaranteed that those patterns are real. If you are looking at random statistical noise, it is not your job to find something in it. It is your job to filter it out and try to find real effects that are hidden behind it. The skill, of course, is figuring out which is which, because you don't want to filter out the real effects. *If* there are any real effects, which sometimes is not the case. |
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Post by statsqueen on Apr 30, 2024 21:19:24 GMT -5
I will say, though, to Mike.... If you have the right data person, it's their job not to stop combing the data until they find something actionable. If you ask the right questions and you can figure out how to quantify it, anything can become data. I have spent my entire career working with highly chaotic data (noise and combustion and turbulent flow). If you have enough randomness, you can find patterns in it, but it is not at all guaranteed that those patterns are real. If you are looking at random statistical noise, it is not your job to find something in it. It is your job to filter it out and try to find real effects that are hidden behind it. The skill, of course, is figuring out which is which, because you don't want to filter out the real effects. *If* there are any real effects, which sometimes is not the case. Of course! That's why you have to approach it as a scientist--with hypotheses and hypothesis testing. THAT is why the ML methods are flawed, and a lot of people don't "get it" because ML sounds cool--and is a very effective way to parse big data if you need to do it quickly. I'm just saying, there are always patterns that mean something under the surface if you're asking the right questions. Sometimes we don't have the data to answer those questions, and that's where you need more than just a number-cruncher. You need a number creator. Someone who knows how to quantify the qualitative. That's the fun part of data science to me.  |
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Post by InTheKnow on Apr 30, 2024 21:55:55 GMT -5
Such nerds.
Coaches stat practices. Connect the code to video. And use the data to teach and evaluate players.
Don’t over think it.
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Post by statsqueen on Apr 30, 2024 22:00:39 GMT -5
Such nerds. Coaches stat practices. Connect the code to video. And use the data to teach and evaluate players. Don’t over think it. Lol that's not all that happens. Patent-pending algorithms resulting in record-breaking performances first match after implementation say you don't have any idea what you don't know, and that's really pitiful. Nerd isn't pejorative. |
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Post by InTheKnow on Apr 30, 2024 22:03:53 GMT -5
Such nerds. Coaches stat practices. Connect the code to video. And use the data to teach and evaluate players. Don’t over think it. Lol that's not all that happens. Patent-pending algorithms resulting in record-breaking performances first match after implementation say you don't have any idea what you don't know, and that's really pitiful. Nerd isn't pejorative. I call bullsh@t. |
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