Now you have to remember that Tiger Woods was so good in the 2000 season that he intimidated every other player. Some of the top caddies thought Tiger had become so good that it was unfair. So Bob May faced a monumental task in trying to beat Woods in that final round.
I relay this story to you because this form of self-hypnosis is the exact thing I want soccer players to do the night before a game. I want them to repeat to themselves exactly how they want to play and what they want their mindset to be like. And the mindset is the important part.
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The problems with emotional regulation might trigger other downstream issues for the child with ADHD. They may, for example, seem to be a few years less mature than their teammates, such as a ten year old appearing to act more like an eight year old. The lack of maturity and ability to control themselves may make it more difficult to make friends and interact with teammates. And in extreme cases, children with ADHD might develop oppositional defiance disorder: a condition where the child gets angry when restricted by a person in authority, whether parent, teacher, coach or referee. Some children may also develop symptoms of depression, particularly if their conditions are undiagnosed and/or left untreated for a length of time.
Great summary of the different variations on the condition. As a coach of several fantastic, soccer-crazy, kids who also have ADHD, I was hoping for more practical advice about how to coach kids with ADHD. How should I design practices? Beyond simple instructions, what are some ways to communicate differently? It would be great to end this article with a list of tips/recommendations for coaching kids with ADHD at the end of the article or recommend another source?
Hola tienen informacin en espaol yo tengo un hijo con ADHD y pase una experiencia muy fuerte y mi hijo tambin todo por que las personas no entienden o no saben mucho de que es esa condicin .. en los equipos de ftbol
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If you're new to data analytics in soccer and want to learn about data sources and datasets available to you, how to get to them and what to expect, you might find the below article of interest. It is meant to give you a quick primer and introduction to the most popular sources known to me at this point, different types of data and their basic applications.
I left the conference excited with what the field had to offer. The excitement of combining two of my favorite subjects, but honestly with very little experience in the combined field itself. So I promised myself to not let the excitement fade and put some time into it and get my feet wet before life takes over. I have a wife, two kids, two dogs and an actual job to maintain. So it took me a bit but I eventually started a small study group with fellow enthusiasts to dive right in.
Having the right, high quality data is harder than I thought and quite honestly I don't think I really have conquered that task in full yet, but it still feels like the right time to share what I learned up to this point.
Most importantly though, I want to note that these are my learnings and my take-aways so far. I am no expert, I am no semi-professional. At times I wonder whether I am even a proper hobbyist, so take what I say below with a grain of salt and double-check with the experts and correct me where you know that I am wrong, in the comments below.
But if you come at this from a similar direction as me, the one of curiosity, you may find some familiar learnings. If you are a hobbyist, who wants to jumpstart your efforts, you've come to the right place. If you are an expert in the field, all this is probably boring beginners stuff.
Applying analytics to soccer sounds simple, doesn't it? Well, it isn't and it starts with a purpose and an audience. Just to name a few, here is a list of individuals and groups that could benefit from soccer analytics:
This list is by no means exhaustive, but I think you get an idea of how big the applicability for soccer analytics is. And with different roles, come different analytics needs. With different analytics needs come different needs for data. Let me dive into a few to highlight the variety you may be looking at:
You want to identify team strengths, weaknesses and gain deep insights into any of those areas and how to minimize, maximize or how to expose them. That depends on what side you're on. You can gather this data and analyze it for your own team (self-scouting) or for the opposing team (opponent scouting). This often happens on the team level.
Additionally your club needs to get a good idea of not just the current team's statistics, but also of potential future prospects for additions to your team. You need to understand the needs of your team and how targeted players could fill that gap and how much they are a match. You will likely want to go well beyond the basic 1-dimensional stats like goals scored per season for attacking players or goals conceded for more defensive positions. It better be a lot more deep than that.
There are a bunch more needs around the operational efficiency of your organization. Certain leagues, like MLS, have complicated financial roster rules and fitting the most talent under that tight salary cap umbrella and optimizing against it is an art. But even in leagues without such salary cap model you have to make the most out of the money given to you and maximize the return of your investments.
That applies not just to the player level, but also the pieces surrounding the club, like fan demographics, attendance records and marketing impact, forecasting and predictions. Whenever there are numbers involved, analytics insights can help lead the way to a more data driven solution to the problem your trying to solve.
Then, maybe with a little less money but pride in the game and bragging rights on the line, there is the huge pocket of Fantasy leagues and their need to optimize their weekly team operations. Virtual or not, here too, success cannot be built on intuition alone. The best strategies are backed by facts, trends and predictions.
Last but not least, there is a whole world of entertainment that needs to be supplemented with the ever growing need for facts and sometimes fiction. Everybody who watched MLS before, has their own level of love or hate for the Audi Player Index. At this point it's just part of the regular programming and while many don't realize it, soccer analytics has certainly invaded the daily soccer programming on your TV, print or web media. It might not seem as noble as "proper" analytics but who is to say that it isn't making money and important. Engaging and stunning visuals on top of your data are very important here and no easy feat.
In addition to defining the consumers of data it is useful to define the types of data usually found and observed in the space. The following is a listing of types I have either observed, personally used or are aware of:
There is likely more, especially when you get more creative with your analytics and what you want to achieve. You combine data or come up with something completely new. But in the current landscape the above probably represents about 90% of the starting points.
Traditionally it started with a lot of manual labor and input. People with pen and paper were watching the game in person, later on tape. From one data crazy person at the side of a soccer field to hoards and hoards of button pushers recording fine details of the game, this discipline was probably optimized to the maximum of manual labor over many decades.
However, nowadays, technology is making progress in leaps and bounds to automate the process and take data collection out of human hands where that technology is available and not cost prohibitive.
There are more and more advanced sensors becoming available every day to collect positional data for both players and the ball in relation to the soccer pitch. Performance sensors are recording vitals along the way and record players ability to withstand the physical and mental pressure of a 90+ minutes soccer game or training session and inform coaches when it's time to take you off the pitch earlier.
The emergence of high definition video recording technologies both in pro-equipment as well as individual devices in our pockets becoming more and more powerful, combined with the powers of machine learning, computer vision and AI, added a whole new dimension to this field of data collection. In fact, in a lot of cases the output is not just data anymore. Often lots of pre-canned insights come pre-built on top of that data. The sky is really the only limit here.
The only real downer is how some of this technology is becoming more and more proprietary and locked down to who has the ability to record it and gets locked away in secret vaults. Then it is only available for a lot of money to those who are ready to buy the rights and licenses to it. But more about that in the next section
It really boils down to this in my eyes: There are the big players in the data game with the big bucks or asking for big money to get you on the in. Then there are the medium sized to smaller operations that operate semi-free or for small(er) fees. And then there are the grassroots operations scraping every tiny source for the much needed data. But in a nutshell you often get what you pay for or how much effort you put into it.
A lot of the freely available data is very basic, boring high level league/season facts/player performance facts or of questionable outdated quality. If you find a free data set that is of higher quality it is often either a sample or hand-made/scraped with a lot of sweat and manual labor to gather that set. Tough to replicate for future seasons. But to the hobbyist, they are still valuable sources to start with. 152ee80cbc
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