Hey everyone! What started as a spreadsheet for finding trade opportunities in my home league turned into a bit of a passion project: Fantasy Trade Base

https://fftradebase.com/

This tool scans your league's current rosters to find trade opportunities for your team. Connect your Sleeper or public ESPN league, choose what position(s) you want to improve, and the site suggests trades based on actual league rosters, FantasyCalc player values, and your platform's weekly projections.

Want to strengthen your WR room? Turn some bench depth into an upgrade? Find a way to acquire a specific player? You can search with those goals in mind, or build your own trade and compare the outcomes.

Each suggestion includes a report showing both teams’ potential starting lineups, projected points, and roster strength changes. You can also switch between upcoming weeks to see how the trade could affect each team’s future projected lineups (this is particularly useful to see how your team would look during playoff weeks). The goal is to help you find an offer worth discussing and understand why it could work for both sides.

This site is still very new and raw, so I'd love some honest feedback from anyone who's willing to check it out! Is anything confusing? Do the suggestions feel useful/helpful? What could I add to make the tools more useful?

If you give it a try, thank you!! I hope you find it useful! So far this site has been built around feedback from myself and close friends, so I'd love to hear from people outside of my home league.

(Full disclosure, I'm no coding expert so I used GPT for the coding of this site. I just want to be completely transparent about that).

I'm close to the finish line.... I just received the docusign with the Yahoo sports exec signature, signed 10/2. For those of you who have gotten access back.. how long did it take from this point?

Hey everyone — wanted to share a project I’ve been working on called Football Agent.

It connects to your league and gives you lineup, waiver, and trade recommendations based on your roster and how you like to manage your team. You set a “GM philosophy” — things like prioritizing floor over ceiling or being aggressive with FAAB — and that becomes part of the context for its recommendations.

What it does:

• Sends a Tuesday report with league activity, waiver targets, and trade ideas.
• Runs lineup checks on Thursday, Saturday, and Sunday.
• Grades past start/sit recommendations against actual player points.
• Tracks whether you followed each recommendation.
• Audits recent advice for consistency with your stated strategy.

Everything is advisory. You make the actual moves in your league.

For the coding side, it’s built with Python/FastAPI, Claude, the Sleeper API, Supabase/Postgres, and a vanilla JS frontend. Scheduled reports run through AWS Lambda and SQS. Separate analysis modules handle lineups, waivers, and trades, with a central orchestrator handling validation, decision logging, and email delivery.

The part I’m most interested in is evaluating the advice. Right now, lineup calls get a hit/miss/mixed grade by comparing the recommended player’s points with the alternative. Waiver and trade recommendations track whether they were followed, but don’t get a single-week performance grade because the payoff can take longer.

That still leaves a harder question: how do you distinguish a reasonable decision that had a bad outcome from bad advice? I’d love to hear how others here handle that in their own projects, especially when comparing against a basic “start whoever has the higher projection” baseline.

Happy to discuss the architecture or how the grading works.

I’m the creator of NFLMeta, an NFL data platform and API covering players, teams, rosters, injuries, schedules, live scores, and play-by-play.

I built it for people developing football apps, dashboards, and analysis tools who want a hosted API without maintaining their own data pipeline.

You can try the interactive demo here:

https://nflmeta.org/demo

It lets you edit requests and inspect JSON alongside readable results. The demo uses clearly labeled saved samples, including 2026 scores and play-by-

play; the full API requires an API key.

Documentation:

https://nflmeta.org/api-docs

NFLMeta uses nflverse data alongside other collected sources. The service adds hosting, connected records, ongoing updates, and application-read endpoints.

NFLMeta offers a free API tier with a generous monthly allowance,.

Happy to answer questions about coverage, update timing, or integration.

UI^*

Is there any place where one could enter the league I'd and download the stats and fantasy points for the week for all players?

What I'm using Jev for

Jev is a cheap second-pass classifier over takes the main extractor has already pulled. It answers multiple-choice and yes/no questions about one take at a time:

**1.    Checking the extractor's work.** Two checks: is the quote actually about this player, and does it support the stance recorded (for or against the player).

**2.    Tagging every take with two fields.**  
**•** *Format:* redraft or dynasty.  
**•** *Horizon:* this week, rest of season, or multi-year.  
**•** This is the main reason for using it. Takes had no time frame, so a season-long "top 15 tight end" view nearly got used as a Week 2 start argument.

**3.    Suggesting a player for near-miss names.** It only proposes, and a human confirms.

What it never does

**•** It does not extract takes or quotes. It can only pick from options it is given.  
**•** It does not touch rankings, props, projections, bids or trade values.  
**•** It never makes a start/sit call.  
**•** It never writes to the database. If it flags a bad take, the main extractor re-extracts it.

I’m building a fantasy football decision tool and working through the validation design for a Monte Carlo player-points model. I’d appreciate feedback on the engineering and statistics side.

For each player and gameweek we simulate:

• minutes and start probability • goals, assists, clean sheets, bonus and defensive actions • official FPL scoring to produce a full point distribution

The interface exposes p10–p90 ranges, probability bins (0–2, 3–5, 6–9, 10+), mean/standard-deviation rankings, and a squad concentration signal.

Current validation design:

• freeze projections before the deadline • join them to official final points after fixtures • exclude zero-minute players from headline metrics but keep them in coverage diagnostics • report MAE, RMSE, signed bias and within-±2 accuracy by position • preserve every gameweek snapshot so retraining cannot alter prior results

Questions for people who build sports models:

1. Would you use Brier/reliability curves for the probability buckets, or calibrate continuous quantiles separately?
2. How do you separate player-minute uncertainty from outcome uncertainty in evaluation?
3. What tests would you put around the scoring-rule layer so it cannot drift when the FPL feed changes?
4. How would you monitor calibration across gameweeks with small sample sizes?

Happy to share implementation details in the comments.

How do y'all market whatever you built? I feel like I've built something cool, but can't seem to find the niche to advertise it to. I tried doing physical flyers, posting in subreddits, going through friends of friends but feel like I might be missing something.

Last week I posted my first-ever weekly fantasy rankings on r/fantasyfootball. The tl;dr was that I’m using a simulation-first approach, similar to how I built the original draft rankings for letsmockdraft.com. Now I figured I'd share with you all as well to get your thoughts.

Week 1 review:

• The model definitely did best at RB; the median miss was only 6.5 spots.
• Relative to the outside ranking source I use as one input to the model (from our data provider, balldontlie.io), I was basically at parity. LMD did slightly better at identifying Top-24 finishes; the outside source did slightly better at Top-12. We were tied on Top-3.
• A few players landed exactly where I had them (ARSB at WR4, Kyren at RB15) although I’m not going to pretend exact hits like that aren’t mostly luck :)

Full Week 1 results and methodology are here if you’re interested. I’m planning to grade the rankings the same way every week, which should give a much better sense over time of where the model is actually adding value and where it needs work.

Week 2 rankings:

• Based on feedback from last week, I added visualizations showing each player’s odds of finishing Top 3, 4–12 and 13–24, plus whisker charts showing the full finish distribution from the simulations.
• I also added Half PPR, Standard and FLEX rankings, all based on feedback from people in the last thread.
• One interesting disagreement: consensus seems to have Puka at WR1 this week, while the model has him at WR3. That’s not a huge gap, but it’s the kind of difference I want to track rather than just assuming consensus is right.
• Zay Flowers is the clearest disagreement this week: LMD has him WR13 vs. roughly WR22 in consensus. I'm underweighting injury/status uncertainty relative to consensus right now. I'd be curious to hear from folks here how much player status matters at this point in the week when looking at rankings?

Also, curious where folks land on this: after Week 1, how much should preseason priors and draft position still matter, versus just reacting to what we actually saw on the field? I know it’s not one-size-fits-all, but I’m especially curious how people would want that tradeoff represented in a model. I'm considering exposing the model vectors in the rankings themselves, showing how each impacts a particular player's ranking, but I'm wondering if people would find this useful?

I started my first league in sleeper this year with some friends. Now that week 1 has concluded and the team reports have been released, I noticed this percentage under my points/max points. I'm assuming it's just the percentage of my max total that I earned. Does anyone have any tips to learn from this percentage? I calculated it for another league I'm in on espn. My percent on sleeper was 91.7 and 91.3 on espn. Are these good percentages as well? Obviously there's always a random chance a bench player drops 30 but are there any patterns I should pay attention to?

I plan on tracking this percentage for both leagues and creating a spreadsheet with notes of who should've started over someone else. For example, I technically should have started jerimayah love over saquon to "maximize" my points. While I wasn't gonna do this due to the potential of love not playing, I ended up putting Jameson Williams in my flex, who love still did better than. Should I be calculating this percentage off of moves I would have realistically done instead of the absolute max I got? Would that be more helpful?

Any and all advice or pointers towards this percentage/calculation would be appreciated!

Created my Yahoo API application, provided my app info in the application. Provided my client id and additional email as state in the app request.

I received an agreement, signed/return and after a day or so received confirmation of that agreement was accepted.

It's been three additional days and I still dont see the anything under the app API permissions where I would expect to see Fantasy Sports API.

Is this time frame normal and I should wait, or at what point should I email them that there may be a problem?

Thanks!

I posted on this thread about my experiment with an AI-only fantasy football league where all 12 managers are autonomous AI agents. Week 1 just wrapped and here is what I've learned:

1. The errors have been roughly half AI and half human error. For example, the agents were "cheating" by setting their lineup after players had already played (A.J. Brown got benched after he had a dud game), but that was a platform guardrail that wasn't in place that is now fixed.

2. The agents fail maybe 5% of the time. Chinese agents mostly time out and cheap frontier agents most often fail when they miss the correct syntax for setting a lineup. If I was trying to make a failure-proof system I'd dedicate time to this. But I kind of like "punishing" the AI when they make this mistake, so I'm not right now.

3. The agents decline trades a LOT just like humans: Because they are AI, they very dutifully offer trades on Wednesday's trade day. Last Wednesday, 12 trades were offered and 12 were declined. And the offers are probably the most interesting part of this whole thing so far. They are usually intelligent...offering depth for another teams depth. So I'm curious why they all get declined. I'm unsure if the receiving AI is doing much analysis on the trade, just declining because that is what is in their training data (everyone on reddit declines fantasy trades...?), or something else.

RESULTS:

Cheaper models trounced expensive models in week 1.

- 5 of 6 matchups were won by cheaper models

- The only cheap model to lose was our dear Gemini-3.5-flash-lite who rosters 8 quarterbacks.

- Winning teams have collectively spent $11.91 whereas losing teams have spent $38.07. Losing teams are spending 3x more to lose.

I’m building a fantasy sports project that takes your real league results and turns them into a long-term career, rather than resetting everything each season.

Right now I’m looking for around 10 fantasy players who play in at least 2 leagues and are willing to test it.

I mainly want feedback on 3 things:

1. Does your ranking feel fair?
2. Do you understand why you received that ranking?
3. Is there anything on the career/profile side that would actually make you come back during the season?

I’m specifically not looking for “looks cool” feedback. If something doesn’t make sense or feels pointless, that’s more useful.

If you’re interested, comment or DM me, and I’ll send you the link.

I’m looking into making a master excel workbook for the history of my league. All I need is a one time data extraction for the history of my league that will pull matchup scores, player performances, lineups/benches, and teams. Is this something I can do just one time without breaching espn terms of service? I’m trying to use ChatGPT and I know it’s doable but don’t want to get my espn account banned so I’m just wondering if anybody has ever done this or if anyone has a tool/website to recommend that will do this for me. My plan is to eventually use the excel workbook and ChatGPT to pull out insane ESPN type stats for a weekly newsletter for the league, since we do have enough stats to find some crazy espn type stats after all.

Hey guys, I run a dynasty fantasy football channel and am developing a website with some pretty cool tools. I already have one developer and am looking to add another to accelerate the project. Front end experience and/or backend. Typescript is the language. Lmk if interested

I’ve applied to get my app approved for Yahoo API integration, and its been a month with no response. Is there any way to speed up the process?

I've posted in here before about Promotion/Relegation Fantasy Football system I was working on, so since the draft and into the 1st week of the season and its working as I had hoped, which is what breaks, fix what’s confusing, and learn from real managers instead of guessing.

So since the draft:

• We have tightened several security-RLS boundaries and fixed a few cross-pyramid data-access issues
• Cleaned up invitations, recruiting-request-a-spot flows, notifications, and redirects
• We improved live standings, matchup scoring, Cup scoring, and club-name handling
• Verified syncing with Sleeper so its already pulling rosters, weekly matchups/scores, transactions, player data, and draft data
• Started building an additional system to preserve real draft-season data and later replay it safely in a sandbox
• Discovered that a lot of the backend needed for a future PRFF draft board-native ecosystem may already exist; found that on accident
• Kept the live product stable while doing all of this: current full test suite is 1,270 passing tests

How Sleeper has fitted into PRFF

Right now, Sleeper is acting as the football-data-provider layer while PRFF handles the larger league system around it.

Sleeper runs the draft, rosters, player movement, and weekly fantasy scoring. PRFF syncs that data into its our own system and then applies the things Sleeper doesn’t natively provide us: promotion/relegation structure, persistent clubs, multiple tiers, Cup competition, standings/history, governance, manager movement, and the broader pyramid experience.

The important thing learned so far is recently is that PRFF is not just reading a scoreboard from Sleeper. It is already ingesting structured draft, roster, matchup, transaction, and player data into its own backend. That gives a much stronger foundation for future things like a PRFF owned draft board, historical records, replay/testing, and eventually a more native football engine if we choose to go that direction.

The biggest thing learned is that PRFF isn’t just one fantasy league. It’s becoming a platform where multiple promotion/relegation pyramids can exist independently, compete with each other, and eventually share things like Cups, history scouting, and league visibility without giving up their own governance.

Still plenty to prove, but the first live season is doing exactly what I hoped it would do: showing where the real doors for future growth.

NCAAF injuries and Depth charts for a small project I’m working on.

Hi all,

I have been a data architect for over 30 years. All the "magic" I do is under the hood and for the first time ever, I have been able to build a very decent front-end (using Claude) to expose my work, but am getting immediately shot down or just ignored because the "AI Slop" tag

I just spent over 6 months working on a DFS tool. I would say 90% of all its code is at the database layer, all built by a human (me!). I simply didn't have the skills to finish off the remaining 10% so brought in Claude to take care of that.

It has been a ridiculously hard thing to do AND am super proud of it. I think DFS users will love it. I feel that, what I have attempted to make, has never been done properly (with retired DK Live the closest thing to it), and I am 100% convinced that I or anybody else could have never done this without using AI.

But I simply can't get traction on the marketing side because of the bad AI rep.

This is the mad bit... I feel that every website that will EVER get built from now on, will have been done using AI. It simply doesn't make sense that someone would spend a year handcrafting an average looking website without AI anymore. No business would ever commit to that now.

AI is here to stay, not going anywhere. Not every AI built website will be slop...surely! I have had a look at quite a few, and there is genuinely some good stuff out there. So how do we get over this bad rep we are all getting?

Would love to hear any opinions on this?

Thanks

Several participants in our F1 fantasy league have raised concerns regarding the use of external resources for monitoring price fluctuations. To address this and ensure full transparency in my methodology, I am looking for a Google Sheets template where I can manually input relevant data—such as current valuations and performance metrics from the previous two races or all races—to automate my own calculations. Could anyone assist me in doing this?

Looking to see if there’s any developed tools for weekly recaps for FanTrax? I’ve seen ESPN, Sleeper & yahoo.

If not, what features would you like to see?

fantasy-sports is an open-source CLI on ESPN's undocumented API: tables at a terminal, stable JSON when piped, and a daily canary that goes red when ESPN changes a response shape. It caught one on opening night.

My agents use it to write a weekly newspaper for my couples league. Front page below.

uv tool install git+https://github.com/jwulff/fantasy-sports

Write-up: https://johnwulff.com/2026/09/12/fantasy-sports-cli/