Supporting Sportstensor – Subnet 41 of Bittensor. Empowering sports data innovation through decentralized AI. Join the crew driving the future of sports tech!
I will be in Vienna for the Numerai conference. If you are there, come say hi. $tao. Also I will be going to grave of Ludwig von Boltzmann to ask his ghost if I should invest in Quantum Rings.
The Sportstensor Meta Model framework is one of our flagship offerings that transforms individual miner predictions into diversified, risk-adjusted capital deployment signals.
The STMM will anchor both the Almanac platform and enterprise solutions while supporting our treasury operations.
Read about it here:
https://t.co/81md9lEjqc
This creates a meta-intelligence that learns not just from individual predictions, but from the patterns of success and failure across the entire subnet.
The STMM then dynamically adjusts miner influence based on historical performance metrics, and continuously optimizes the aggregation function to maximize prediction performance.
While traditional crowd wisdom simply averages opinions, ensemble learning employs weighting algorithms to identify which models are most reliable for specific purposes.
Imagine having access to the combined intelligence of dozens of the world's best sports prediction models, each with their own unique approach to forecasting game outcomes.
Here's what we're working on:
Sportstensor would like to share a new article which describes how a subnet can design its incentive mechanism to mathematically strictly gaurantee no waste in emissions and that there is no way to possibly design a better I.M.
Enjoy! $tao #tao#bittensor#thisoneisforstriker
"Stop Paying for Noise: The Optimization Rule for Subnet Incentives"
https://t.co/DxydanNyh7
Quietly in $tao our resident COO on @sportstensor just kicked the living crap out of a scamming inference copier called "Michael" who has been gaming our MLB system for 2 months. Here is what happened.
When MLB started we purposefully allowed for a greater edge to be acceptable than normal. The purpose? To allow for principal agent strategies to run wild for a bit so we could collect information.
1) A guy spotted up nearly 120 uid's and ran a copying range of inference accross all the bets, slightly changing them a bit to avoid detection with the simple detector.
2) The multi accounter then noticed he could just bet favorites in the mlb by large margins, knowing they would win and he could run up a big edge score and get emissions. He fell right into the honeypot!!
By allowing this to happen we gathered up all their account data and were able to run a statistical similarity test on all of the accounts which involved a clustering algorithm. We could not have built this without the data; Thank you Michael!
Our COO then pulled the trigger like a boss and murdered all of his accounts and then "Michael" went on a huge rage in our discord, accusing the subnet of fraud and wrong doing.
This is how you combat fraud. Sometimes you have to let it go for a bit to get the data you need to create a crushing blow to dirty miners and at the same time make Bittensor less wasteful and respect $tao holders and not give emissions to bad actors. We probably now have the most sophisticated copy controller in Bittensor bar none.
Sorry Mike! but you will never beat us, you are not smart enough. We will smash you into dust every time. Only one guy beat us badly in sn-41 history and that is lecrodank and he is a good guy who deserved it!.
Duplicate miner submissions plague almost every prediction subnet. They add noise and UID pressure.
Our first detection system was rather successful. However, recent analysis revealed persistent noise that affected prediction quality, requiring a more sophisticated solution.
The Prediction Integrity Controller works as an advanced plagiarism detection for predictions. It analyzes miner behavior across multiple time intervals, tracking identical outcome choices and statistical correlation in probability assignments to identify systematic copying.
Key technical improvements include processing 4x more data across all prediction intervals, using Spearman correlation to detect copying even when noise is added, and implementing graduated penalties with proper statistical rigor instead of binary responses.
The system incorporates safeguards like minimum sample size requirements to prevent false positives, while its tiered penalty structure scales consequences with copying severity rather than applying harsh all-or-nothing punishments.
This advancement benefits the entire ecosystem: miners face fairer evaluations that reward genuine effort, Almanac users receive higher-quality predictions, and Sportstensor strengthens its foundation for broader market adoption through enhanced integrity.
Almanac is ready for launch but…
Major improvements are being made to our incentive mechanism that will positively impact the predictions you see on Almanac. We’ve decided to delay the launch tentatively for a few weeks so subnet updates can take effect.
Subnet improvements are for an even better Almanac experience.
Conversations with institutions looking to utilize our intelligence has led us to dive deeper into the performance of our miners.
Tweaks to our incentive mechanism are coming to further improve outputs for commercial use.