Alright, let’s dive into this baseball thing I’ve been messing with – calculating MVR, or Most Valuable Replacement.
So, it all started with me wanting to put a number on just how valuable a player really is compared to, well, anyone else you could just stick out there. You know, your average Joe off the bench.
First off, I needed some data. I grabbed a bunch of stats from a baseball stats website – think batting average, home runs, RBIs, all that jazz. Cleaned it up a bit in a spreadsheet – got rid of the fluff and the weird outliers that were messing things up. You always gotta do that, right?
Then came the tricky part – figuring out how to actually calculate MVR. I started by trying to find the ‘replacement level’ player. This is basically your scrub, the guy who’s easily available and doesn’t cost much. I figured out roughly what his production would be across the board based on averages of players who barely make the roster.
Next, I compared each player’s stats to this ‘replacement level.’ So, for example, if a star player hit 30 home runs and the replacement level guy would hit 5, the difference (25 home runs) is part of what makes that star valuable. I did this for a bunch of different stats – runs scored, RBIs, stolen bases, the whole shebang.
But just adding those differences wouldn’t work, right? Hitting a home run is way more important than stealing a base. So, I had to figure out a way to weight each stat. I did some research online and played around with different formulas until I found something that felt right – basically, something that lined up with what I already knew about baseball and player value.
I plugged everything into a script, ran the numbers, and BAM! I had an MVR number for each player. The higher the number, the more valuable they are compared to a replacement-level player.
Here is how i approached each aspect:
- Data Collection: Searched for baseball stats datasets on websites like Baseball-Reference, and FanGraphs.
- Data Cleaning: Used spreadsheet to clean the data to remove missing values, outliers, and irrelevant columns. Ensured data consistency.
- Replacement Level Determination: Analyzed historical data of players with minimal impact to estimate the performance metrics of a typical replacement-level player.
- Performance Metrics: Calculate the differences between each player’s stats and those of a replacement-level player across various categories (e.g., runs, RBIs, stolen bases).
- Weighted Score: Used research to apply weights to stats to align with their impact on team performance.
- MVR Calculation: Combined weighted values to derive a single MVR score for each player, representing their value above a replacement-level player.
It’s not perfect, of course. There’s always room for improvement and different ways to weigh the stats. But hey, it was a fun project, and it gave me a way to look at player value in a different light. It helped me appreciate some players that I wouldn’t always appreciate.
Plus, now I can sound like a real know-it-all when I’m watching games with my buddies. Which is always a plus, right?
