{"id":1058,"date":"2019-10-07T00:00:00","date_gmt":"2019-10-07T00:00:00","guid":{"rendered":"https:\/\/leadersinsport.com\/performance-institute\/reports\/data-and-analytics-how-to-avoid-confirmation-bias-and-groupthink-through-datasets\/"},"modified":"2025-12-15T05:12:28","modified_gmt":"2025-12-15T05:12:28","slug":"data-and-analytics-how-to-avoid-confirmation-bias-and-groupthink-through-datasets","status":"publish","type":"article","link":"https:\/\/leadersinsport.com\/performance-institute\/articles\/data-and-analytics-how-to-avoid-confirmation-bias-and-groupthink-through-datasets\/","title":{"rendered":"Data and Analytics: How to Avoid Confirmation Bias and Groupthink Through Datasets"},"content":{"rendered":"\n\n
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\n Oct 07, 2019 <\/p>\n \t\t\t\t\t\t\n\t\t\t\t\t\t\t<\/span>Articles<\/a>\n\n\t\t\t\t\t\t

Data and Analytics: How to Avoid Confirmation Bias and Groupthink Through Datasets<\/h1>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\n \n\t\t\t<\/div>\n\t\t<\/div>\n\n\t<\/div>\n<\/div>\n
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Category<\/div>\n Data & Innovation<\/a> <\/div>\n
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Share<\/div>\n Facebook<\/a>\n Twitter<\/a>\n Email<\/a>\n Copy Link<\/a>\n
https:\/\/leadersinsport.com\/performance-institute\/articles\/data-and-analytics-how-to-avoid-confirmation-bias-and-groupthink-through-datasets\/<\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n\n\n
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Cutting through the data white noise in search of the desired outcome in elite sport.<\/p>\n \n \n \n

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\u201cYou need to be aware of confirmation bias\u201d says Tom Batchelor, the Lead Sports Science & Conditioning Coach at Harlequins.<\/span><\/h4>\n
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By John Portch<\/h6>\n

\u201cEveryone is going to make decisions that are biased in<\/span>\u00a0their nature but sometimes you\u2019re aware of it and hopefully you don\u2019t fall into that trap too often.\u201d<\/span>\u00a0<\/span><\/p>\n

Karl Cooke, Performance Team Director at the Western Australian Institute of Sport, adds that groupthink is a constant threat. \u201cIt\u2019s human nature and there\u2019s a whole bunch of things that can cause us to make bad decisions,\u201d he observes. \u201cSometimes in the heat of the battle, you don\u2019t have time to take in all this information.\u201d<\/span>\u00a0<\/span><\/p>\n

He then poses a key question: \u201cCan we use technology to cut out the noise and present our teams with pertinent information?\u201d\u00a0<\/span>Here, we look at some of the ways data can be used to ensure your team does not lose sight of the outcome or goal.<\/span>\u00a0<\/span><\/p>\n

Always c<\/span><\/b>hallenge conventional thinking<\/span><\/b>\u00a0<\/span><\/p>\n

It is hard to argue against firm evidence<\/span>\u00a0and,<\/span>\u00a0<\/span>i<\/span>n the right hands<\/span>,<\/span>\u00a0it can be used to challenge the sort of groupthink that can stem from a collective approach<\/span>. \u201cEvery sport will have its conventions,\u201d says Cooke<\/span>. \u201cWe build metrics of say defence or attack and, to take the former, you build up intelligence on the really important things for evaluating defensive performance. You start to blend the knowledge and experience of key coaches with an evidence base from the sport.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Ryan Murray, Director of Baseball Analytics at the Texas Rangers, says: <\/span>\u201cPeople are making decisions based on the information you are providing them<\/span>.\u00a0<\/span>Say the coach wants a visual of the strike zone by batting average, my first response to that is to tell them that it\u2019s not very predictive of anything.<\/span>\u201d He explains it to them in terms that never lose sight of the overarching goal.<\/span>\u00a0<\/span>\u201c<\/span>Let\u2019s actually break down into what the coach is trying to understand and how we can get to that goal. I might tell them that our internal metric, a combination of exit velocity and launch angle and hit direction, is much more predictive. You try to meet them on their plain and make it an ongoing conversation. If they\u00a0ask\u00a0\u2018why is this section highlighted red?\u2019 we can really open up a dialogue that allows you to do build that trust with the people who are ultimately making the decisions.\u201d<\/span>\u00a0<\/span><\/p>\n

At Harlequins, says\u00a0 Batchelor: <\/span>\u201c<\/span>W<\/span>e have the sort of environment where rather than go for something new, we\u2019re going to go for something better<\/span>.\u00a0<\/span>It might turn out that that better thing is something that might have existed for a long\u00a0time\u00a0but dat<\/span>a and information helps to put proof to it.\u201d<\/span>\u00a0<\/span><\/p>\n

Challenge yourself in what you collect<\/span><\/b>…<\/span><\/b>\u00a0and be ready to be challenged by others<\/span><\/b>\u00a0<\/span><\/p>\n

Whether it is coaches<\/span>\u00a0<\/span>o<\/span>r support staff, your decision-making is going to be better\u00a0<\/span>informed\u00a0<\/span>with a workforce ready and willing to challenge the status quo. They can also help ensure the data stays focused on performance and not the hunch of a data scientist. \u201cI can dig and I can look,\u201d says Chad Gerhard, the former Applied Sports Scientist at the Orlando Magic, \u201cbut you can very quickly disappear down a rabbit hole and it doesn\u2019t matter<\/span>. I\u2019d much prefer people to prod and poke me. It happens a lot here, whether it\u2019s the medical staff or the strength & conditioning staff, and it\u2019s good for me to be challenged.\u201d<\/span>\u00a0<\/span><\/p>\n

At Harlequins, Batchelor finds himself taking the role of devil\u2019s advocate on a regular basis: \u201cI end up being not anti-data but \u2018anti some of the reasons that are brought for data\u2019 because there are a lot of examples out there of bad data and you have got to be aware of false positives and self-fulfilling prophecies.\u201d<\/span>\u00a0He adds that much of it can come down to recruitment. \u201cFrom the off you deliberately try to not just recruit people who completely agree with your perspective. We certainly have a lot of\u00a0<\/span>conversations in our team from different perspectives on subjects without individuals pulling it in different directions.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

It is an approach that has also served David Martin well during his time working in the NBA with the Philadelphia 76ers. \u201cThose group meetings can be messy<\/span>, emotional and sometimes argumentative but I welcome the chaos and like to brainstorm across a lot of different areas.\u201d Ultimately, Martin\u2019s team will disagree and commit. \u201cThere will be a delivery to coaching staff and players and those messages need to be simplified and they have to have uniform support.\u201d<\/span>\u00a0<\/span><\/p>\n

Data models can also be created t<\/span><\/b>hat<\/span><\/b>\u00a0challenge convention<\/span><\/b>\u00a0<\/span><\/p>\n

It is not only staff but analytic predictive models that can serve to challenge conventional thinking. \u201cMachine learning has made things more interesting for those using predictive models,\u201d observes Cooke. \u201cA traditional model would have been our experience telling us that an athlete is vulnerable to injury when these things happen. Now, with tools like machine learning, over time we can train the algorithms to say these are the red flags for this athlete; and they\u2019re evidence-based from a database of training and injury inflammation. That\u2019s exciting in that we have the traditional model there and then sitting alongside that maybe a more objective machine learning-based\u00a0<\/span>to challenge our traditional thinking. We might continue to stick with the traditional model but at least we\u2019re challenging our thinking with some other ideas as well.\u201d<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

On occasion, it can lead to a revised approach, as was the case at the Texas Rangers, where Murray introduced the team\u2019s first defensive algorithm prior to the 2015 offseason and the result was an increased number of outs that pushed the team to the division championship. \u201cWe taught coaches where the defenders need to stand at any given point on the field,\u201d he recalls. \u201cThis was something that had never been automated before; the coaches talked about defensive positions and I wasn\u2019t trying to replace that functionality \u2013 I was trying to augment it. We were doing analysis throughout 2015 and we decided that the algorithm was bringing us roughly 182 outs over the course of a season, which is 1.1 outs per game, all from being in the correct positions.\u201d<\/span>\u00a0<\/span><\/p>\n

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The Sport Performance Summit London<\/strong><\/p>\n

Data & Innovation is one of the four key content pillars that will be covered at our flagship event this November 12-13 to prepare our members to out-think, out-prepare and out-perform the competition. Click here<\/a> to find out more about the event.<\/p>\n<\/p>\n <\/div>\n \n \n \n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n","protected":false},"excerpt":{"rendered":"

Cutting through the data white noise in search of the desired outcome in elite sport.<\/p>\n","protected":false},"featured_media":1059,"menu_order":0,"template":"","categories":[23],"pathway":[],"topic":[],"sport":[],"class_list":["post-1058","article","type-article","status-publish","has-post-thumbnail","hentry","category-data-innovation"],"acf":[],"yoast_head":"\nData and Analytics: How to Avoid Confirmation Bias and Groupthink Through Datasets - Performance Institute<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/leadersinsport.com\/performance-institute\/articles\/data-and-analytics-how-to-avoid-confirmation-bias-and-groupthink-through-datasets\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data and Analytics: How to Avoid Confirmation Bias and Groupthink Through Datasets - 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