エピソード
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Today go through an indepth planning of GW 12, GW 13 and beyond. Walking through a few different options. Also I introduce a new Discord Server for the podcast and general FPL chat. Come join the coversation here. We can complain about differentials getting lucky and Haaland blanking for the nth time this season. And a return of 'Anything but FPL.'
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.
Track: "TOMOLI", T-Pain
Music provided by https://Slip.stream
Free Download/Stream: https://get.slip.stream/gYqcMB
Listen on Spotify: https://go-stream.link/sp-t-painSupport the show
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Today I rank the top 15 attacking FPL assets (Forwards + Midfielders) based on a combination of value added per million (VAPM), VAPM using the underlying data model, explosiveness, captaincy opportunities, predictability, availability, and price gains. Later I discuss my performance from GW10, another good (probably lucky) week, and plans for GW11 and the big GW12.
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.
Track: "TOMOLI", T-Pain
Music provided by https://Slip.stream
Free Download/Stream: https://get.slip.stream/gYqcMB
Listen on Spotify: https://go-stream.link/sp-t-painSupport the show
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エピソードを見逃しましたか？
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This is a part 2 follow-up on the casestudy of stupid mistakes in FPL from last episode (Gameweek 8 to Gameweek 9). I evaluate every iteration of my stupidity and walk through the thought process of each decision. Later I discuss planning for the big Gameweek 12 fixture swing and why I will be moving on from Haaland.
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.
Track: "TOMOLI", T-Pain
Music provided by https://Slip.stream
Free Download/Stream: https://get.slip.stream/gYqcMB
Listen on Spotify: https://go-stream.link/sp-t-painSupport the show
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Mistakes have been made. It is time to do a deep dive into my mistakes: Why am I making early transfers, and why am I compounding that by taking a (-4) hit? Today's episode is more of a case study in bad decisions and trying to understand the thought process of doubling down on mistakes made early in the season.
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
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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
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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
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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
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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.Support the show
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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.Support the show
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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
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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.Support the show
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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.Support the show
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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.Support the show
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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-seazinSupport the show
