Good morning. A tactical strength coach outlines safe speed training for first responders, KINEXON argues sports tech is shifting from isolated tools to unified infrastructure, and researchers use AI to predict doping violations in elite weightlifting. Let's get into it...
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News
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New Jobs
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Events
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Mens’s Basketball AP Poll Update
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Arizona |
Chris Rounds |
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Michigan |
Matt Aldred |
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Houston |
Alan Bishop |
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Duke |
Preston Greene |
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Iowa State |
Pete Link |
🚔 Speed Training for Tactical Athletes
Brandon Holder, S&C coordinator for Fairfax County Police, published a progression model for implementing speed and movement training with first responders.
Tactical athletes often prioritize strength and endurance but neglect movement qualities critical for survival. Holder's model starts with 5-10 yard distances, extensive plyometrics (pogos, skips), and 2-3 sets per movement, progressing to resisted sprints, hill work, and scenario-based drills. Key safety rules: follow a plan, stop if quality drops, avoid concrete, and don't combine speed with high-fatigue circuits. Read More
☕️ Coffee, Caffeine, and the Brain
A massive new JAMA study followed 131,000+ adults for up to 43 years and found that caffeinated coffee and tea intake were linked to a lower risk of dementia and slightly better cognitive function. The sweet spot wasn’t “more is better” — the strongest associations showed up around 2–3 cups of caffeinated coffee per day (or 1–2 cups of tea). Read More
📊 Sports Tech Is Shifting from Tools to Infrastructure
KINEXON Sports published a 2026 outlook arguing that sports tech is no longer evaluated by innovation alone but by real-world workflow integration. Organizations want unified systems across wearables, tracking, and video—not fragmented tools.
Five trends: technology convergence is becoming standard, real-time is now baseline expectation, movement intelligence is scaling, leagues expect one implementation to unlock multiple outcomes (integrity + media), and adoption is the real competitive advantage. Read More
🤖 AI Might Be Coming for Doping Detection
Andy Galpin flagged a proof-of-concept paper testing whether AI can help detect doping in sport. Using publicly available data (past performances, height, weight, etc.) plus confirmed doping-violation records, the model showed 53% predictability among female Olympic weightlifters from the 2008, 2012, and 2016 Games. It’s early and limited, but it suggests performance/biometric patterns may be useful for identifying higher-risk cases. Read More
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