Alright, so yesterday I kept seeing folks online buzzing about Victor Wembanyama. Tons of comments like “Wemby is on fire!” or “What’s up with the Spurs lately?”. Got me curious – just how good has he been playing lately? Specifically, his last 10 games. I figured, instead of just guessing, why not dig into the numbers myself? Needed some fresh stuff for the blog anyway.
The Painful Starting Point
First thought was easy: just grab the stats from the league website, right? Wrong. Big headache. You go to a player’s page, it shows you season totals or maybe the last 5 games if you’re lucky. Trying to get exactly the last 10 games felt like looking for a needle in a haystack without a magnet. Clicked around forever – game logs, splits, advanced stats – but getting a clean table showing just those specific 10 recent games? No dice. Felt super clunky. Almost gave up right there.
The “Aha!” API Moment (Kind Of)
Then I remembered – there’s gotta be an API for this stuff! Did a quick search, found one. Got excited. Downloaded Python on my machine (again) and started messing around with some sample code to pull player game logs. This part got messy:
- Figuring out the right endpoint? Trial and error.
- The data came back in a format that looked like alphabet soup dumped into a spreadsheet.
- Needed to filter it down to ONLY Victor and ONLY his last 10 games played.
It wasn’t smooth. Lots of errors, lots of googling how to fix them. Felt like wrestling an octopus made of code for a solid hour. But eventually, bam! Got a list of his last 10 games with all the raw numbers.
Crunching the Numbers (Basic Style)
Okay, had the data. Time to see what it said. Didn’t go crazy with advanced stats here, kept it simple since I just wanted a quick snapshot. Focused on the big ones everyone talks about:
- Points per game: Added ’em all up, divided by 10. Simple.
- Rebounds per game: Did the same thing – total rebounds, divided by 10.
- Blocks & Assists per game: Same process again for each.
- Shooting %: Took all his shots made, divided by all shots taken. FG%. Did the same for his 3-pointers and free throws.
Also jotted down if there were any games missing – like, did he not play in one? Had to check the game logs again to make sure.
Spotting the Trends (The Fun Part)
Looking at the averages was okay, but the real story started showing when I looked at the numbers game-by-game. Plotted the points on a quick sketch.
Holy smokes, the highs were HIGH! One game he goes off for a monster number, next game maybe a bit quieter. His scoring wasn’t a flat line, it was like a crazy rollercoaster. Same with rebounds and blocks – huge nights mixed with just “good” nights. But crucially, hardly any bad nights. Even his “quiet” games were still putting up numbers most big guys dream of.
The shooting percentages were wild too. Some games he couldn’t miss, others… you could tell he was figuring things out. But overall, the average efficiency looked solid for a rookie handling his workload. Especially the free throws – dude was hitting them pretty consistently, which is huge for a big man.
What Stood Out? Putting it Together
So, after all that clicking, downloading, filtering, and adding up, here’s the juice:
- Massive Averages: The points, rebounds, blocks combo over just 10 games? Insane for a rookie. Even by star standards, it’s impressive.
- Consistent Impact: He might have a quieter scoring night once in a while, but he was ALWAYS doing something big – swatting shots into the stands, grabbing key boards, making a smart pass.
- Efficiency Trending Okay: Sure, he has cold shooting nights. But the overall picture across those 10 games showed pretty decent efficiency, especially considering the pressure and how much he does.
- Development Obvious: You could see little adjustments game to game. How teams were guarding him changed slightly, and you could spot where he tried something new that worked (or didn’t!).
End result? All that online buzz suddenly made total sense. The raw numbers backed it up, but seeing the trends in those 10 games – the crazy peaks, the lack of real valleys – that’s what showed how special this run was. Dude is legit figuring it out faster than anyone expected. Glad I dug into it myself instead of just taking someone else’s word for it!
