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Post by coachb5212 on Oct 23, 2019 9:34:02 GMT -6
Coaches,
What websites or resources do you guys use to study football analytics? I am a big numbers and statistics guy and I love studying these things in relation to football to hopefully give myself more of an advantage to win games. Thanks!
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Post by wingtol on Oct 24, 2019 5:55:40 GMT -6
Analytics in HS football are a waste of time if you ask me. There is such a disparity of talent on the field in most places that you're not getting anything out of it. Not a fan of them in college and the pro game either but at least in those situations the teams are full of football players where HS you have lots of guys who just play football. You want an advantage in HS watch and break down their film to find the match up mis matches
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Post by coachd5085 on Oct 24, 2019 19:10:48 GMT -6
Coaches, What websites or resources do you guys use to study football analytics? I am a big numbers and statistics guy and I love studying these things in relation to football to hopefully give myself more of an advantage to win games. Thanks! I think the vast majority of guys here would agree with wingtol Any type of wide ranging "analytics" are going to be relatively useless in football because the talent disparity is so frequent. |
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Post by vanden48 on Oct 24, 2019 21:17:18 GMT -6
R2 Sports Technologies. I started a thread on it. It is the money ball for football.
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RnS-OC
Sophomore Member
Posts: 117 
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Post by RnS-OC on Oct 25, 2019 0:24:01 GMT -6
If you are diligent in labeling your own film on hudl you can run most analytics with their custom report feature. It's useful for teams that heavy tendencies and for self-scouting.
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Post by carookie on Oct 25, 2019 10:11:12 GMT -6
As other have stated above, the talent disparity in high school ball really limits the effectiveness of analytics. In the NFL (and high levels of college ball), where talent is virtually homogeneous, then tactical decisions based on broad statistical analysis makes sense.
In HS, where one team has multiple power 5 players and the other has kids who wear polo shirts under their pads, then you can throw money ball out the window in regards to in game tactics- the team with better athletes can do everything against the analytic book and still win by 50. Heck, they could skip the whole week of practice, line up with only 10 and probably pick their margin of victory.
Its just hard to get meaningful, across the board, accurate info in this environment.
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Post by coachcb on Oct 25, 2019 10:39:42 GMT -6
The high school game is so unpredictable, given the disparity in talent and the "variety" of schemes... So, IMO, analytics are a waste of time. Honestly, there are weekends where breaking down tendencies via HUDL of another team's offense has been a waste of time.
This week is a perfect example. We got our hands on two films of the team we're playing. One is a total blow-out, from start to finish; they were down 21-0 in the first quarter. They were trying to out-formation a dominant team all game so we got a good luck at all of their formations and their passing schemes but that's it.
The next film is a tight game but the opposing defense was doing some seriously goofy stuff that we'll never run. The run/pass ratio by D&D -might- be useful this week but the formation/play tendencies are useless. They ran the Down series again and again because the opposing defense was giving them a HUGE off-tackle gap. We're not going to give that to them..
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Post by vanden48 on Oct 25, 2019 13:18:22 GMT -6
Analytics is not for everyone and you have to have a good understanding of data to be able to use it efficiently. As most have pointed out here, close games in high school are not the norm. However, if you have a team that is close to your talent, your ability to dissect information can be the difference between winning and losing. Practicing analytics for weaker teams helps you prepare to use them for stronger teams, when you will need them. Using data takes the "gut feeling" coaching out of it. I have found that in close games, finding a tendency that the data reveals has been the difference. For me it has been which plays the favor running to the field or to the boundary in situations. This allows me to select the best defense for that call. Not knocking coaches who don't use data, as we all know there are 100 different ways to do something. I have found that older coaches are less inclined to use data. I'm a science teacher, so data is what I do and I am very familiar with how to use it. Sometimes there is so much data you don't know what to do with it.
So first know if you have the ability and time to enter data.
Then decide what data is important to you. Don't waste time on things you won't use. I know that sounds obvious, but it is worth stating.
Third decide how the data will help you prepare for practice and the game. I use the data to prepare my scout cards and practice call sheet. And I write my game call sheet every week using tendencies from data.
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Post by wingtol on Oct 25, 2019 13:48:38 GMT -6
As other have stated above, the talent disparity in high school ball really limits the effectiveness of analytics. In the NFL (and high levels of college ball), where talent is virtually homogeneous, then tactical decisions based on broad statistical analysis makes sense. In HS, where one team has multiple power 5 players and the other has kids who wear polo shirts under their pads, then you can throw money ball out the window in regards to in game tactics- the team with better athletes can do everything against the analytic book and still win by 50. Heck, they could skip the whole week of practice, line up with only 10 and probably pick their margin of victory. Its just hard to get meaningful, across the board, accurate info in this environment. LMAO "Polo shirts under their pads" it's funny because it's true. One of our kids did that this year... |
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Post by carookie on Oct 25, 2019 16:40:31 GMT -6
As other have stated above, the talent disparity in high school ball really limits the effectiveness of analytics. In the NFL (and high levels of college ball), where talent is virtually homogeneous, then tactical decisions based on broad statistical analysis makes sense. In HS, where one team has multiple power 5 players and the other has kids who wear polo shirts under their pads, then you can throw money ball out the window in regards to in game tactics- the team with better athletes can do everything against the analytic book and still win by 50. Heck, they could skip the whole week of practice, line up with only 10 and probably pick their margin of victory. Its just hard to get meaningful, across the board, accurate info in this environment. LMAO "Polo shirts under their pads" it's funny because it's true. One of our kids did that this year... Its my new way to describe that type of kid. Buddy called me a few weeks ago to congratulate me on a win, asks how the season is going. conversation follows: Me: "Yeah, we're in trouble now though because our starting left guard and Sam linebacker is out for the year." Him: "We'll how good our his backups." Me: "Ugghh, it'll be the same kid; and well, he's the kind of kid who wears a polo shirt under his pads." Him (laughing): "I know exactly the type of kid you mean." I'm glad to hear we aren't the only ones |
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Post by coachdubyah on Oct 25, 2019 18:00:03 GMT -6
LMAO "Polo shirts under their pads" it's funny because it's true. One of our kids did that this year... Its my new way to describe that type of kid. Buddy called me a few weeks ago to congratulate me on a win, asks how the season is going. conversation follows: Me: "Yeah, we're in trouble now though because our starting left guard and Sam linebacker is out for the year." Him: "We'll how good our his backups." Me: "Ugghh, it'll be the same kid; and well, he's the kind of kid who wears a polo shirt under his pads." Him (laughing): "I know exactly the type of kid you mean." I'm glad to hear we aren't the only ones Doesn’t offer anything to the thread but I had a dad deliver a Pink BEACH TOWEL for his son to wear on his belt...the towel was as tall as me. I couldn’t let myself allow that kid to wear that. |
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Post by coachd5085 on Oct 25, 2019 18:37:51 GMT -6
Analytics is not for everyone and you have to have a good understanding of data to be able to use it efficiently. As most have pointed out here, close games in high school are not the norm. However, if you have a team that is close to your talent, your ability to dissect information can be the difference between winning and losing. Practicing analytics for weaker teams helps you prepare to use them for stronger teams, when you will need them. Using data takes the "gut feeling" coaching out of it. I have found that in close games, finding a tendency that the data reveals has been the difference. For me it has been which plays the favor running to the field or to the boundary in situations. This allows me to select the best defense for that call. Not knocking coaches who don't use data, as we all know there are 100 different ways to do something. I have found that older coaches are less inclined to use data. I'm a science teacher, so data is what I do and I am very familiar with how to use it. Sometimes there is so much data you don't know what to do with it. So first know if you have the ability and time to enter data. Then decide what data is important to you. Don't waste time on things you won't use. I know that sounds obvious, but it is worth stating. Third decide how the data will help you prepare for practice and the game. I use the data to prepare my scout cards and practice call sheet. And I write my game call sheet every week using tendencies from data. Are you discussing individual game/opponent scouting? I think that is a different deal than what people are referring to as "analytics" |
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Post by vanden48 on Oct 26, 2019 14:34:02 GMT -6
What are you considering analytics?
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Post by coachd5085 on Oct 26, 2019 14:42:31 GMT -6
What are you considering analytics? Analytics would be data mining large sources of data to make broad "assumptions". For example, becoming a team that never punts because "analytics" show it is better to go for it each time, using things such as "expected point values" etc. Essentially I think in on this board, "analytics" would be using large quantities of data that don't necessarily apply to the particular game/opponent to make decisions for said opponent. Scouting a team and seeing that they throw screens 65% of the time on 3rd and long, so you call a zone blitz or just send 3 or 4 isn't what I think most here would say is "analytics". Not calling trap right in week 7 , even though it was "analytically" your best play the first six weeks because the opponent happens to have an SEC player at 3 tech would be the opposite of analytics. scholar.rose-hulman.edu/cgi/viewcontent.cgi?article=1002&context=engineering_management_grad_theses Something like that. Now, I do think the analytics that we see so prevalent in baseball has a place in football. That place however, is simply to make the coach think about decisions, not automatically go with data. It is similar to the difference between tournament poker and a cash game if you have any poker knowledge. |
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Post by coachebbe on Oct 31, 2019 11:09:48 GMT -6
R2 Sports Technologies. I started a thread on it. It is the money ball for football. Do you use it? What services do you use? What's the cost? |
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Post by gccwolverine on Oct 31, 2019 11:21:51 GMT -6
What are you considering analytics? Analytics would be data mining large sources of data to make broad "assumptions". For example, becoming a team that never punts because "analytics" show it is better to go for it each time, using things such as "expected point values" etc. Essentially I think in on this board, "analytics" would be using large quantities of data that don't necessarily apply to the particular game/opponent to make decisions for said opponent. Scouting a team and seeing that they throw screens 65% of the time on 3rd and long, so you call a zone blitz or just send 3 or 4 isn't what I think most here would say is "analytics". Not calling trap right in week 7 , even though it was "analytically" your best play the first six weeks because the opponent happens to have an SEC player at 3 tech would be the opposite of analytics. scholar.rose-hulman.edu/cgi/viewcontent.cgi?article=1002&context=engineering_management_grad_theses Something like that. Now, I do think the analytics that we see so prevalent in baseball has a place in football. That place however, is simply to make the coach think about decisions, not automatically go with data. It is similar to the difference between tournament poker and a cash game if you have any poker knowledge. I disagree with alot of you guys on the "talent is to wide for analytics at the HS level." Increasing win expectancy is increasing win expectancy regardless of what the gap was. If we're 90-10 to lose to an opponent but doing x (like never punting, or going for 2, or always on siding) results in a small uptick of win expectancy then although we might now be 88-12 to lose we have given ourselves the smallest of improved chances. Additionally I think you want to hammer every possible risky edge you can hammer in games where you are out talented because it gives you a better chance to win. It might also increase the chance of the game getting uglier sooner however. ***In fact I think that because of the large talent disparities that its most often true that individual game or 3 game breakdowns don't tell the whole story of a team because they either see really weak competition or competition so good that they never had a chance. "They run down 65% of the time" - well yea but that's because the team they played weak 2 had 165lb Jimmy 2 Left feet playing defensive end and also lined up with a 1 and a 9 technique 30% of the time. Does that mean that's who they are to their core or were they just taking advantage of what was given to them? In contrast large data mining analytics that focus on win equity and expediencies based on decisions or actions in any individual game tend to be more true simply due to the law of large numbers. |
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Post by coachcb on Oct 31, 2019 11:37:43 GMT -6
Analytics would be data mining large sources of data to make broad "assumptions". For example, becoming a team that never punts because "analytics" show it is better to go for it each time, using things such as "expected point values" etc. Essentially I think in on this board, "analytics" would be using large quantities of data that don't necessarily apply to the particular game/opponent to make decisions for said opponent. Scouting a team and seeing that they throw screens 65% of the time on 3rd and long, so you call a zone blitz or just send 3 or 4 isn't what I think most here would say is "analytics". Not calling trap right in week 7 , even though it was "analytically" your best play the first six weeks because the opponent happens to have an SEC player at 3 tech would be the opposite of analytics. scholar.rose-hulman.edu/cgi/viewcontent.cgi?article=1002&context=engineering_management_grad_theses Something like that. Now, I do think the analytics that we see so prevalent in baseball has a place in football. That place however, is simply to make the coach think about decisions, not automatically go with data. It is similar to the difference between tournament poker and a cash game if you have any poker knowledge. I disagree with alot of you guys on the "talent is to wide for analytics at the HS level." Increasing win expectancy is increasing win expectancy regardless of what the gap was. If we're 90-10 to lose to an opponent but doing x (like never punting, or going for 2, or always on siding) results in a small uptick of win expectancy then although we might now be 88-12 to lose we have given ourselves the smallest of improved chances. Additionally I think you want to hammer every possible risky edge you can hammer in games where you are out talented because it gives you a better chance to win. It might also increase the chance of the game getting uglier sooner however.
That's the pitfall; you might end up chasing ghosts just to lose by a larger margin. Or, worse, apply the analytics to a team you can handle and end up losing because of it.
As a DC, I'm extremely conservative when it comes to game planning around tendencies which I would consider "analytics". We broke down a two very tight games when planning for a "spread" team that had an overall 70-30% pass-run ratio. We were meticulous in breaking the offense down by D&D, field position, etc..etc.. We hammered that game plan during the week; extra pass skelly sessions, repped our five-man pressures, etc..etc..
They spent half of the game in their goal line/short yardage 32 personnel package... We repped adjusting to that formation in ONE goal line session as we only saw them pull it out a handful of times on film. Our initial adjustment didn't handle it well so I blew a timeout, made some personnel changes and that slowed it down.
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Post by gccwolverine on Oct 31, 2019 11:41:38 GMT -6
I disagree with alot of you guys on the "talent is to wide for analytics at the HS level." Increasing win expectancy is increasing win expectancy regardless of what the gap was. If we're 90-10 to lose to an opponent but doing x (like never punting, or going for 2, or always on siding) results in a small uptick of win expectancy then although we might now be 88-12 to lose we have given ourselves the smallest of improved chances. Additionally I think you want to hammer every possible risky edge you can hammer in games where you are out talented because it gives you a better chance to win. It might also increase the chance of the game getting uglier sooner however.
That's the pitfall; you might end up chasing ghosts just to lose by a larger margin. Or, worse, apply the analytics to a team you can handle and end up losing because of it.
As a DC, I'm extremely conservative when it comes to game planning around tendencies which I would consider "analytics". We broke down a two very tight games when planning for a "spread" team that had an overall 70-30% pass-run ratio. We were meticulous in breaking the offense down by D&D, field position, etc..etc.. We hammered that game plan during the week; extra pass skelly sessions, repped our five-man pressures, etc..etc..
They spent half of the game in their goal line/short yardage 32 personnel package... We repped adjusting to that formation in ONE goal line session as we only saw them pull it out a handful of times on film. Our initial adjustment didn't handle it well so I blew a timeout, made some personnel changes and that slowed it down.
I added to my post prior to seeing your quoting of it. But I hit on this a bit in the post above. |
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Post by carookie on Oct 31, 2019 17:29:17 GMT -6
Analytics would be data mining large sources of data to make broad "assumptions". For example, becoming a team that never punts because "analytics" show it is better to go for it each time, using things such as "expected point values" etc. Essentially I think in on this board, "analytics" would be using large quantities of data that don't necessarily apply to the particular game/opponent to make decisions for said opponent. Scouting a team and seeing that they throw screens 65% of the time on 3rd and long, so you call a zone blitz or just send 3 or 4 isn't what I think most here would say is "analytics". Not calling trap right in week 7 , even though it was "analytically" your best play the first six weeks because the opponent happens to have an SEC player at 3 tech would be the opposite of analytics. scholar.rose-hulman.edu/cgi/viewcontent.cgi?article=1002&context=engineering_management_grad_theses Something like that. Now, I do think the analytics that we see so prevalent in baseball has a place in football. That place however, is simply to make the coach think about decisions, not automatically go with data. It is similar to the difference between tournament poker and a cash game if you have any poker knowledge. I disagree with alot of you guys on the "talent is to wide for analytics at the HS level." Increasing win expectancy is increasing win expectancy regardless of what the gap was. If we're 90-10 to lose to an opponent but doing x (like never punting, or going for 2, or always on siding) results in a small uptick of win expectancy then although we might now be 88-12 to lose we have given ourselves the smallest of improved chances. Additionally I think you want to hammer every possible risky edge you can hammer in games where you are out talented because it gives you a better chance to win. It might also increase the chance of the game getting uglier sooner however. ***In fact I think that because of the large talent disparities that its most often true that individual game or 3 game breakdowns don't tell the whole story of a team because they either see really weak competition or competition so good that they never had a chance. "They run down 65% of the time" - well yea but that's because the team they played weak 2 had 165lb Jimmy 2 Left feet playing defensive end and also lined up with a 1 and a 9 technique 30% of the time. Does that mean that's who they are to their core or were they just taking advantage of what was given to them? In contrast large data mining analytics that focus on win equity and expediencies based on decisions or actions in any individual game tend to be more true simply due to the law of large numbers. The talent discrepancy comment is meant to dissuade someone for creating analytics based on HS football data. The premise being data from HS games will be so heavily influenced by talent discrepancy that you won't be able to easily isolate the variable that led to success. Its not saying don't use tactical advantages, its saying be wary what you think you might see from charting data from high school football as a whole. Your second point is valid, but I think you can get some solid tendencies that hold true with enough film- particularly what teams run from given formations. But agree that without a significant sample size they can be misleading. |
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Post by Coach Vint on Nov 3, 2019 23:19:29 GMT -6
I include a score differential column. Up 28/down 28 or more and we throw the data out. You also have to consider everything else, including who they were playing and what the did, and consider that as you interpret data. I love the data we get on our opponents and from our self scouts. Data gives you a good picture of your opponent. But it doesn’t tell the whole story.
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