{"id":750,"date":"2020-07-07T00:00:00","date_gmt":"2020-07-07T00:00:00","guid":{"rendered":"https:\/\/leadersinsport.com\/performance-institute\/reports\/five-tips-for-challenging-existing-processes-and-systems-through-data\/"},"modified":"2025-12-15T05:12:15","modified_gmt":"2025-12-15T05:12:15","slug":"five-tips-for-challenging-existing-processes-and-systems-through-data","status":"publish","type":"article","link":"https:\/\/leadersinsport.com\/performance-institute\/articles\/five-tips-for-challenging-existing-processes-and-systems-through-data\/","title":{"rendered":"Five Tips For Challenging Existing Processes and Systems Through Data"},"content":{"rendered":"\n\n
\n\t
\n\n\t\t
\n\t\t\t
\n\n\t\t\t\t
\n \n\t\t\t\t\t
\n\t\t\t\t\t\t

\n Jul 07, 2020 <\/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

Five Tips For Challenging Existing Processes and Systems Through Data<\/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
\n
\n
\n
Category<\/div>\n Data & Innovation<\/a> <\/div>\n
\n
Share<\/div>\n Facebook<\/a>\n Twitter<\/a>\n Email<\/a>\n Copy Link<\/a>\n
https:\/\/leadersinsport.com\/performance-institute\/articles\/five-tips-for-challenging-existing-processes-and-systems-through-data\/<\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
\n
\n
\n
\n

A series of suggestions brought to you by the San Antonio Spurs and Wests Tigers.<\/p>\n \n \n \n

\n

Are you making the best of your data? The continuous process of working out where data can have its greatest impact in performance leads to inevitable stress-testing of your data models.<\/span><\/h4>\n
\n
By John Portch<\/h6>\n

Xavi Schelling, the Director of Sports Science & Performance at the San Antonio Spurs and Andrew Gray, the High Performance Manager at Wests Tigers, have both worked across a series of sports organisations across a range of sports, some with more developed data and analytics programmes than others, but there are some tenets to which they hold true.<\/span>\u00a0<\/span><\/p>\n

The Leaders Performance Institute presents their five tips for ensuring you are still finding and using the correct tests and metrics as your sport continues to evolve.<\/span>\u00a0<\/span><\/p>\n

1. Be sure to iterate and reiterate<\/span><\/b>\u00a0<\/span><\/p>\n

At San Antonio, Schelling describes the cycle that sees performance processes continually challenged with a basketball department. \u201cMy main goal as a director\u202fof\u202fperformance and\u202fsports\u202fscience, is\u202fto ask the right questions\u202fto the\u202fright\u202fpeople. This\u202fapplies\u202falso to the coaches,\u202fso the\u202fbetter\u202fthe questions\u202fthey ask us, the easier\u202fit is\u202ffor\u202fus\u202fto build a\u202fbetter\u202ftool for\u202fthem.\u201d<\/span>\u00a0<\/span><\/p>\n

\u201cYou deliver\u202fsomething [a decision support system] that hopefully will increase the knowledge of the organisation, leading to new questions or optimising old ones.\u00a0 That feedback is necessary to improve the next iteration of the system or tool. This dynamic process is\u202fsomething that has\u202fto\u202fbe\u202fconstant, not just for\u202fnew\u202freports\u202for\u202fnew tools but also to optimise old tools; and this\u202fis\u202fcritical.<\/span>\u00a0<\/span><\/p>\n

\u201cDon\u2019t try to keep\u202fimplementing new devices, new tools\u202fand new technology, also invest time in reviewing, \u2018OK, I created this\u202ftool and implemented this\u202fdevice, we generated this\u202fknowledge, we are\u202fbetter\u202fand now there are more and\u202fbetter\u202fquestions\u202fgo back and optimise the\u202fsame\u202freport or\u202fthe\u202fsame tool to use it more efficiently.\u2019 Once this\u202floop\u202fis\u202fexhausted, you have maximised the\u202fservice of this\u202ftool or\u202fdevice, then you move on and think of implementing\u202fsomething else. But it\u2019s\u202fcritical to understand where the\u202fpeople are in terms\u202fof knowledge and needs and build from there.\u201d<\/span>\u00a0<\/span><\/p>\n

2. Identify inconsistencies, keep the good, and discard the rubbish<\/span><\/b>\u00a0<\/span><\/p>\n

\u201cThe problem when moving between\u202fclubs,\u201d says Gray, \u201cis there isn\u2019t necessarily\u202fconsistency in how a\u202ftest\u202fis being performed or exercises that are\u202ftested in the gym.\u201d It can, in certain circumstances, make efforts to blend old and new datasets problematic. \u201cThose\u202fcan be troublesome and the easiest thing to do\u202fcan be to not blend old and new datasets – but to throw it out and\u202fstart\u202fagain but then you\u2019re missing the opportunity to learn from that.\u201d It is a balance. \u201cIf there were four\u202fstrength movements that were being\u202ftested in an old dataset and we have another\u202ffour\u202fthat are being\u202ftested now and only two of them are\u202fconsistent then we\u2019ll probably only\u202fcarry through those two. There needs to be a fair level of\u202fconsistency between\u202ftests if we\u2019re\u202fgoing to try and extrapolate anything from it.\u201d<\/span>\u00a0<\/span><\/p>\n

3. If you can, run older and newer solutions side by side<\/span><\/b>\u00a0<\/span><\/p>\n

Sometimes the challenge is not a change in device but a software or firmware update on an existing device. Nevertheless, as Schelling explains, the same questions remain. \u201cThe first question is\u202fwhether or not updating that firmware or\u202fincluding a new device or\u202fupdating the device is\u202f really\u202fbetter\u202fthan what you had?\u201d he says, \u201cfirst you should invest time in making\u202fsure that the vendor\u2019s claims are true and that\u202fit\u2019s\u202fnot just a marketing tool. If the device or software really is\u202fbetter\u202fbecause it\u2019s\u202fmore\u202fprecise and reliable, or because it brings new actionable information, then update or\u202fchange as\u202fsoon as\u202fpossible. If it\u2019s\u202fbetter\u202fthan what you had in the\u202fpast,\u202fthen do not hesitate; acquire it and implement it as\u202fsoon as\u202fpossible.<\/span>\u00a0<\/span><\/p>\n

\u201cNow, if you can afford it, overlap\u202fsystems, meaning I have the old device and the new\u202fdevice\u202fand I use them both for\u202fa\u202fseason, the old and the new at the\u202fsame time. This\u202fway you can have a very accurate assessment of variability\u202fbetween old and new data and you can come up\u202fwith\u202fsolutions\u202fto that variation.\u201d<\/span>\u00a0<\/span><\/p>\n

4. Try to balance the benefit of a tool against the drawbacks of its inaccuracies<\/span><\/b>\u00a0<\/span><\/p>\n

Gray is clear on the matter: \u201cI think it\u2019s important to understand the variability in any measurement and if we bring a new device into a system we spend a lot of time understanding that margin for error or that variability and if it\u2019s too great then it just doesn\u2019t fit.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cBut if we\u2019re not hung up on a device that has a degree of variability, as long as we\u2019re able to understand it because it may be that it enables us to\u202fcapture data in an area where we might otherwise be unable to; and it may have some beneficial effects. Sometimes I think we may become too hung up on the fact that there is variability within this measurement and exclude that device from our system and throw the baby out with the bath water.\u202f<\/span>\u00a0<\/span><\/p>\n

\u201cWe also\u202fcan\u2019t\u202fgo\u202fdown the other end and be making big decisions on players and not looking to deeply understand the error measurements. I think there\u2019s a middle ground there; and that middle ground is where we try to operate to get the benefits of using the technology, the benefits of the data and\u202fcreate a\u202fconsistent language, but understand what those issues are and, in a sense, try to give\u202fconfidence intervals and be open and discuss everything.\u201d<\/span>\u00a0<\/span><\/p>\n

5. Be mindful that vocabulary can change along with a device<\/span><\/b>\u00a0<\/span><\/p>\n

The \u2018language\u2019 of data was discussed in a previous chapter and your team\u2019s data vocabulary can shift with the adoption of a new device. \u201cThat\u2019s the issue for teams, especially if they were using a device for a while and building really\u202fgood language around that, then the device\u202fchanges; all of a\u202fsudden\u202fthe language\u202fchanges,\u201d observes Gray. \u201cThat\u2019s a real issue in performance data.\u201d Tread carefully when innovating and stress-testing your data modelling.<\/span>\u00a0<\/span><\/p>\n

\n

Looking for more performance insight?<\/strong><\/p>\n

Performance<\/em> 21<\/em> is available for download now and leads with a selection of insights lifted from our At Home With Leaders podcast series, which has featured the likes of England Rugby\u2019s Eddie Jones<\/strong>, the Toronto Blue Jays\u2019 Mark Shapiro<\/strong>, and Chelsea\u2019s Emma Hayes<\/strong> speaking directly from their home offices.<\/p>\n