I'd like to build a web app that is a sports stats/simulation combination game.

The basis of the game is you get to pick one player at each position - LF, CF, RF, 3B, SS, 2B, 1B, C, SP, RP so 10 players total.

The way you choose a player is by pressing a "SPIN" button that randomly picks an MLB team and year from anytime in the last 50 years.

You are then presented with the players and their statistics on that team and that year as options to fill one of the empty slots you have not filled in your lineup. There will be 10 spins to fill each of the 10 spots. You can fill the spots in any order you wish, but only 1 player for each spot. So, if you pick a starting pitcher from the 1985 Cardinals for your first pick, you can no longer pick a starting pitcher on the next turn.

After all roster spots have been filled, we will run a simulation to evaluate/predict what the record would be for the team based purely on the statistics of those players from only the year/version of that player.

Things we need for this:

1) We need to build a database of players and years and statistics from each year. It would be like:

For hitters:

Player, Team, Year, Batting Avg, Runs, HR, RBI, SB

For pitchers:

Player, Team, Year, Wins, Losses, Saves, ERA, WHIP, SO

So if we do not have those statistics we cannot build this. That is the first question I need answered. If yes, we should proceed.

Comments URL: https://news.ycombinator.com/item?id=48436681

Points: 1

# Comments: 1

So after some thought, I switched to a system of individual evidence gathering and weighting each piece of evidence. I've given the models some basic starting points for types of evidence (for instance a donation has a default weight of 8/10), but have given the models leeway to make relative judgements.

After all evidence is collected, the weights and confidence that the evidence is accurate (usually very high) are put into a formula to derive a final score. No recency bias. The nitty gritty:

-Each row contributes direction × weight × confidence × status_factor, where disputed is cut in half and there is no recency decay.

-All signed contributions are summed into S, and total support mass goes into M. Final score is 50 + 50 * (S / (M + 4)), clamped to 0-100.

-That +4 prior mass keeps thin but unanimous evidence from producing extreme scores too easily.

-Neutral evidence (direction = 0) doesn’t push the score up or down, but it does increase M, which pulls the result back toward 50.

As for the ladder - I think that is a good idea, but in a controlled manner because of the token cost and potential for abuse.

This is fully AI-coded, researched, and sourced.

Additionally, AI helped develop the scoring system. The evidence gathering is done by a number of different agents through OpenRouter that gather and classify source-backed claims. The point of that is not to pretend bias disappears, but to avoid me manually selecting the evidence myself. I intend for it to remain current and grow. The system is close to fully automated, though ongoing evidence collection at scale is still limited mostly by cost.

The name is an homage to the early days of Web 1.0 and Hot or Not, which was a main competitor of mine as the creator of FaceTheJury.com, but I think it works well here.

The backend and frontend are running on Cloudflare Workers with D1. It's coded in vanilla JavaScript.

Comments URL: https://news.ycombinator.com/item?id=47480192

Points: 7

# Comments: 3

https://stockdaddy.io/samples/dashboard/

They offer an excellent API for a very reasonable price and offer it free to people if you're interested in a more comprehensive solution for free. Also, take a look at IEX if you're interested in getting into financial APIs.

For me it is my dog.

IEX is pretty good with the markets they give access to, so you should be able to find most symbols (or comparable) you're looking to track.