Ignore the Template FPL Podcast – Détails, épisodes et analyse

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 algorithmic 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. 

Support the show

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. 

Support the show

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. 

Support the show

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
Music provided by https://Slip.stream 
Free Download/Stream: https://get.slip.stream/Rr5ppi
Listen on Spotify: https://go-stream.link/sp-seazin

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Today I discuss the concepts of the eye-test, and compare players who pass or fail the 'eye-test' in the context of their underlying and observed statistics. This one is a very subjective topic so feel free to roast my opinions. Later, I rationalize Cole Palmer's 25-pointer, talk briefly about the watch-list, and review my Game Week 6 performance 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. 

Support the show

Buckle in, it is a long one today. I discuss performances from every single team in the Premier League so far this season and give my top 3 FPL assests from each team. All stats provided in this episode are updated through game week 7.

Chapters are in! So feel free to jump around to any team you are particularly interested in.

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. 

Support the show

Over yet another international break I discuss Game Week 8 Wildcard, and how it differs from Game Week 4 WC all within the context of Value Added per Million (VAPM). I turn back to the FPLOptimiseR package in R to examine algorithmically optimized teams. Is threemium in? I also discuss hindsight, and the drawbacks of VAPM, later I review my team and quickly talk through my thought processes.

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. 

Support the show