Today I talk through suggested players for Wild Card Game Week 6 using Value Added per Million (VAPM), use the FPLOptimiseR package in R to examine algorithmically optimized teams, review my team from Game Week 5 and quickly discuss transfers and plans moving forward.

Google Sheet with all of the data: Link

Link to my Reddit for all of my weekly FPL content: Link

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

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Today make a weak defense of the game week 4 wild card, and why it may not have been such a bad idea. I discuss variance and try to make sense of the incredibly weird week in FPL just past. Later I review my team's performance and plan ahead for game week 5, 6 and beyond.

Also side note - I am a complete idiot for trying to do simple math in my head. I now know the average of 110 and 36 is 73, not 74, lol.

Google Sheet with all of the data: Link

Link to my Reddit for all of my weekly FPL content: Link

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

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Today I take wild cards for game week 4 a step further and use algorimic optimization using the FPLOptimiseR package in R. Looking ahead to this weekend I discuss a captaincy deep dive to compare Salah and Haaland by examining their underlying stats with their upcoming fixtures against Forest and Brentford respectively.

Google Sheet with all of the data: Link

Link to my Reddit for all of my weekly FPL content: Link

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

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Today I talk through general Wild Card Strategies ahead of Game Week 4 using Value Added per Million (VAPM), review the massive (and unexpected) green arrow from GW 3, and discuss my WC draft for GW 4 and more.

Google Sheet with all of the data: Link

Link to my Reddit for all of my weekly FPL content: Link

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

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Fantasy Premier League strikes again for Game Week 2. Today I look ahead to Game Week 3 and beyond to discuss high VAPM (Value Added per Million) players, what the data is telling us about them, and target fixtures to boost our ranks.

I also review my GW 2 performance and talk about my plans for Game Week 3. Potential Wild Card opportunities, and more.

Google Sheet with all of the data: Link

Link to my Reddit for all of my weekly FPL content: Link

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

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Fantasy Premier League is back for Game Week 1. Today I discuss overperformers and underperformers looking at player points, and what the underlying data tells us.

I also review my GW 1 performance and talk about plans for Game Week 2.

Google Sheet with all of the data: Link

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

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It's a brand new FPL podcast for a brand new FPL season. Today I will be walking though 'sleeper' picks, or lower owned players with relatively high Value Added per Million (VAPM).

All statistics noted on the podcast were calculated in R with the assistance of the following packages: understatr, fplr, and FPLOptimiseR.

Track: "curiously strong", seazin
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