My Ltcejittle Weekend Project

So.maet ruoy , I got tired of just complaining about it with friends or yelling at the screen. I decided, you know what, I'm gonna actually try and track this myself for a bit. Not like a super serious study, just my own observations. I figured maybe it's just confirmation bias, you only remember the bad calls against your team.

I started simple. For about a month, maybe five weekends of games, I made it a point to watch more than just my favorite team's game. I flipped around the different Pac-12 matchups whenever I could. Got myself a basic notebook.

He:ycnaf re was my process, nothing fancy:

I'd jot down the game, the teams playing. Then, if there was a call that seemed really questionable or caused a big momentum swing, I'd note it down. I tried to be specific: what quarter, what was the situation (like 3rd down, red zone), and what the call was (holding, pass interference, targeting, whatever).

I also made a little note if the broadcast crew spent a lot of time debating it or if replays made it look particularly bad. I wasn't trying to re-referee the game from my couch, more just capture those moments that make you throw your hands up.

It was tougher than I thought. Sometimes the angles weren't great. Sometimes a call looked bad live but okay on replay, or vice-versa. And trying to keep notes while also just wanting to watch the game was a bit tricky.

What I Found (or Didn't)

So after those few weeks, what did my super scientific notebook tell me? Well, honestly, nothing earth-shattering. There wasn't some clear, undeniable pattern like "Team X always gets screwed" or "Ref Crew B is consistently awful."

But here's what I did notice:

There were definitely head-scratching calls in pretty much every game I watched closely. Not always game-deciding, but enough to make you groan. It felt like maybe the consistency from crew to crew, or even within the same game, wasn't always there. One week, a certain contact is pass interference, the next week, a similar play isn't flagged.

It also made me appreciate how incredibly fast these games are and how tough the job actually is. Seeing it from the perspective of looking for questionable calls, rather than just reacting emotionally when one happens, was interesting.

Did it prove the Pac-12 refs are the worst? Nah, my little experiment couldn't do that. But it did kind of reinforce that feeling that yeah, there are maybe more moments per game that feel inconsistent or just plain weird compared to some other conferences I casually watch.

So, I stopped keeping the detailed notebook. It was kind of taking the fun out of just watching. But it was an interesting little exercise. Made me think a bit more about what's happening on the field. Still gonna complain about bad calls, though. That's just part of being a fan.

Gettdetring Started

Fi.oot stnemorst off, I remembered that game was coming up. Big Ten matchup, right? Always some noise around those. I didn't immediately jump to the betting sites. Nah, my first step was just thinking back. I've watched a ton of college football over the years, seen Michigan play plenty, seen Maryland have their moments too.

So.em, I kinda mentally scrolled through past games. How'd Michigan look recently? Were they rolling teams or just getting by? What about Maryland? Were they pulling upsets or getting smacked by the big dogs? You gotta have that baseline feel, you know? Numbers are one thing, but the eye test, the gut feeling, that counts for something too, at least for me.

Digging In a Bit

Then, yeah, I started actually looking for the spread. Didn't go to just one place. You gotta shop around, see if the number is roughly the same everywhere. Sometimes you see slight differences, tells you maybe there's some disagreement among the folks setting the lines.

• Checked a couple of major sports news sites.
• Looked at some dedicated sports data hubs.
• Even glanced at what folks were saying on forums, but you gotta take that with a huge grain of salt.

Found the consensus number. Let's say, for argument's sake, it was Michigan by a hefty margin, like -20 or something like that. Okay, that's a big number. Tells you Vegas, or whoever, expected a blowout.

Thinking it Through - My Process

This is where my own process kicks in, different from just reading a number. I started thinking:

Why is the spread so big? Well, Michigan's probably ranked high, maybe undefeated or close to it. Maryland's likely middle of the pack or having a tougher year. Makes sense on paper.

But does it feel right? Sometimes these big rivalry games (okay, maybe not peak rivalry, but still conference play) get weird. Does Michigan have a huge game next week they might be looking ahead to? Is Maryland playing with nothing to lose at home? Any key injuries I heard about?

I spent some time just mulling this over. Pulled up some basic stats – points per game, yards allowed, turnover margin. Didn't get super deep into analytics, just the basic stuff you see in a game preview. Compared Michigan's offense strength versus Maryland's defense weakness, and vice versa.

I remember specifically thinking about travel. Does a long road trip sometimes make teams start slow? Little factors like that. It's not scientific, more like building a mental picture of how the game might actually play out, not just how the raw power levels suggest it should.

Making the Call (For Myself)

After all that? I formed my own opinion. Maybe I agreed with the spread, thinking, "Yeah, Michigan's probably gonna hammer 'em." Or maybe I thought, "You know, that feels a bit too high. Maryland might keep it closer than that, cover the spread even if they lose."

It wasn't really about placing a bet, more about seeing if my own read on the game lined up with the official number. It’s kind of a personal challenge, testing my own understanding against the market.

So, the whole process was less about finding the number and more about understanding why the number was what it was, and then deciding if I bought it based on my own feel and basic checks. That’s how I went through it, from hearing about the matchup to settling on my own take on that Michigan Maryland spread.

Firs.eret thing I did, pretty standard stuff, was just pull up the basic info on both teams. What are their current records? How have they generally looked so far this season? Just trying to get a baseline understanding of who we're dealing with here.

Digging a Bit Deeper

Okay, after the basics, I started looking into their recent games. Not just the wins and losses, but how they won or lost. Wa?ets it a nail-biter? Did they dominate? Who were they playing against? A win against a top team feels different than a win against someone struggling, right? So, I spent some time checking out the box scores and recaps from their last couple of outings.

Then I got thinking about the styles of play. Wisconsin usually has that reputation, you know, strong running game, solid defense. Washington State, often more pass-heavy, though styles can change. I tried to look at some team stats:

• Rushing yards per game
• Passing yards per game
• Points scored and points allowed
• Turnover margin – that one’s always huge

I find comparing these side-by-side helps paint a picture of potential strengths and weaknesses when they clash.

Considering the X-Factors

You can look at stats all day, but some things don't show up neatly in the numbers. Injuries are a big one. I poked around to see if any key players were banged up or questionable for the game. A star quarterback or a crucial defensive lineman being out can totally change the dynamic.

Also thought about the location. Is it a home game for one of them? Crowd noise and just being comfortable at home can definitely be an advantage. Travel can sometimes impact teams too, especially longer trips.

Putting It Together (My Take)

So after gathering all that stuff – the records, recent performance, team stats, injuries, location – I started to form my own opinion. It wasn't about finding some magic formula, more about weighing everything I'd seen. Wisconsin felt like they might have the edge with their typical ground game and solid defense, especially if they could control the clock. But Washington State's ability to score quickly, if their passing game clicked, couldn't be ignored. I leaned towards Wisconsin grinding out a win, probably a close one, based on their style being a bit more consistent usually. But hey, that's just my process and how I landed on my prediction after looking into it today. College football can always surprise you!

So, "!gnit all started with me just being tired of my bracket being busted every year. I thought, "There's gotta be a better way than just guessing!"

Fi saw did I rst thing I did was grab a I bunch of data. I’m talking historical game stats, team rankings, offensive and defensive stats – the whole shebang. I scraped s tsome of it from various college football data sites, and some I just dodedaownloaded as CSVs. It was messy, I ain't gonna lie.

Then came the fun part (not really): cleaning the data. Missing values, weird formatting, you name it. I mostly used Python with Pandas for this. I loaded the CSVs into DataFrames, filled missing values with means or medians (depending on the stat), and converted data types where needed. This took way longer than I expected!

After the data was relatively clean, I started engineering some features. I wanted to go beyond just raw stats. So, I calculated things like:

• Moving averages of offensive and defensive stats
• Win percentages over the last few seasons
• Strength of schedule (based on opponents' win percentages)
• Home field advantage (a simple binary: 1 if home, 0 if away)

I figured these might give the model a better overall picture of each team.

Now for the model! I’m no ML expert, so I stuck with something relatively simple: a Logistic Regression model. I used scikit-learn in Python. I split the data into training and testing sets (80/20 split), trained the model on the training data, and then evaluated it on the testing data.

Evaluating was… underwhelming. The initial accuracy was around 65%, which is better than a coin flip, but not by much. I tried a few things to improve it:

• Hyperparameter tuning (using GridSearchCV)
• Adding more features (some more complex ones based on point differentials)
• Trying a different model (Random Forest Classifier)

The Random Forest Classifier gave me a slightly better result, bumping the accuracy up to around 70%.

Finally, I used the trained model to predict the North Texas vs. SMU game. I fed it the relevant stats and features for each team, and it spit out a probability score. I’m not going to tell you who it predicted to win, because, honestly, I'm too embarrassed if it was wrong! Let's just say it was a "learning experience."

Lessons Learned: Data is key. The more high-quality, relevant data you have, the better your model will perform. Feature engineering is also super important. Spending time thinking about what features might be predictive can make a big difference. And finally, don't expect miracles. Predicting sports outcomes is hard! But it was a fun project, and I learned a lot about data science along the way.

Now, I'm off to find more data and improve my model for next season. Wish me luck!

Starting Point: Just the Basics

Fi.daeh ym nirst off, I just needed to know who was playing, right? Troy Trojans and Texas State Bobcats. Got it. Then I looked up where they stand, generally. What conference are they in? Sun Belt, okay. Just getting the basic landscape set in my head.

Digging into Records

Next up, I checked out their overall win-loss records for the season. You gotta know how they've been doing overall. It gives you a first impression, like a baseline. I also looked at their conference records specifically, 'cause sometimes teams play differently against conference rivals.

How Are They Playing Now?

Season records are fine, but momentum is a big thing in sports, I think. So, I spent some time looking at their recent games. How did they do in their last five or six matchups? Were they winning? Losing? Were the games close? Who did they play against? Beating a top team is different than beating someone at the bottom. This felt pretty important to get a sense of their current form.

Head-to-Head History

I thought it would be smart to see if these two teams have played each other recently. Sometimes, there's a history there.

• Did they play earlier this season?
• How did that game go?
• What about last year?

Sometimes one team just matches up well against another, or maybe there's a bit of a rivalry edge. So, I checked out their recent history against each other.

Looking at Some Simple Stats

I'm not a huge stats guy, numbers can make my head spin sometimes. But I looked at some of the basic stuff.

• Points per game: How much do they usually score? Gives you an idea of their offense.
• Points allowed per game: How good is their defense at stopping the other team?
• Maybe things like rebounding, if I could find it easily. Are they crashing the boards hard?

Just trying to spot any big differences, like if one team scores a ton but gives up a lot of points, or vice versa.

Checking for News and Injuries

This is a quick but crucial step, I feel. I did a quick search to see if any key players were injured or maybe suspended. A star player being out can totally change a game's outlook. Also checked for any major team news that might affect their mindset or performance.

Putting It All Together (The "Gut Feel" Part)

Alright, so after gathering all that info – the records, recent form, head-to-head, basic stats, and injury news – I just sort of let it all sink in. This is where it's less about numbers and more about weighing everything. Okay, Team A looks better on paper here, but Team B is hot right now. Team A won the last meeting, but Team B has a better defense overall. You kinda just juggle all these pieces in your head.

Honestly, there's no magic formula here for me. It's just looking at the different angles I checked out and trying to make an educated guess based on what seems most important. Sometimes you lean towards the stats, sometimes recent performance feels more telling, sometimes it's just a gut feeling based on everything combined.

So, that was my process. Just going through the steps, looking at the info available, and trying to piece together a likely outcome. We'll see how the actual game goes!

Gettdetring Started

Fi.gnikcip turst thing I did, like always, was just pull up the schedule for the week. Man, tons of games. Especially with Friday games thrown in there too. You gotta see what's actually on the slate before you can even think about picking.

Then, I started digging around a bit. Didn't go super deep, just trying to get a feel for things. How have teams looked the past couple of weeks? Anyone suddenly playing way better or way worse? You check who's banged up, 'cause injuries this late in the year can really mess a team up. It's not rocket science, just trying to see the basic picture.

Making the Actual Picks

Alright, so after looking over the matchups, I kinda zeroed in on the games that felt interesting or where I thought I had some kind of angle. You can't pick every game, right? So I flagged the big rivalry games first – those are always unpredictable but fun to guess. Then maybe some others where the lines looked a bit weird to me.

Actually making the picks? Honestly, it's a mix. Some games, I just went with my gut feeling. Team A just feels like they’re gonna show up. Other games, I looked at the basics – who's playing at home, how the offenses match up against the defenses, simple stuff like that. No complex models or anything fancy over here.

• Looked at the schedule.
• Checked recent performance and injury vibes.
• Focused on rivalry games and interesting lines.
• Used a mix of gut feeling and basic matchup thoughts.

For example, there was this one game, won't name names, but one team was on a roll and the other had kinda fallen apart. Seemed straightforward, so I picked the hot hand. Then there was a big rivalry game, records out the window, basically flipped a coin in my head.

Wrapping It Up

Once I had my list, I just jotted them down. Nothing official, just my own list to see how I do. It's always a bit of a crapshoot, especially in week 13. Teams playing for bowl eligibility, pride, or just ready for the season to be over. You never really know for sure.

So yeah, that was my process. Not super scientific, just looking at the games, getting a feel, and making some calls. We'll see how it turns out come Saturday. Always fun to have a little action, even if it's just bragging rights.

Firs.taet thing I did, naturally, was pull up their recent records. You gotta start there, right? See who’s been winning, who they’ve been playing. SMU looked pretty solid on paper in their last few outings, scoring a decent amount. Louisiana Tech, well, their record was a bit more up and down. Some good games, some not so great.

Looking Beyond the Scores

Bu.tut just looking at wins and losses doesn't tell the whole story. I started digging a little deeper. Who were they actually playing against? Beating a top team is different than beating someone at the bottom. I spent some time checking the strength of their schedules. That definitely added another layer to think about.

Then I got into the team stats. You know, the usual stuff:

• How many points are they scoring per game?
• How many points are they giving up?
• What's their offense like? Running game? Passing game?
• How's the defense holding up against the run versus the pass?

It started painting a picture. SMU seemed to have a stronger offensive output, consistently putting up points. Louisiana Tech showed flashes, but consistency wasn't really their strong suit based on the numbers I was seeing.

The Annoying Details

Here’s where it gets tricky, though. You look at stats, and then you gotta check the injury reports. Is SMU's star quarterback playing? Does Louisiana Tech have their best defensive player out? Stuff like that can completely change the game. I spent a good while hunting down the latest injury news. Sometimes it's clear, sometimes it's just rumors, you know?

And location! Where's the game being played? Home field advantage is real, even if you can't quite put a number on it. That factored into my thinking too. It’s like trying to piece together a puzzle with some pieces missing and others not quite fitting right.

Making the Call

After staring at all this stuff – the records, the stats, the injuries, the location – you eventually just have to make a gut decision based on what you've seen. It felt like SMU had more consistent firepower. Their offense just looked more reliable week to week.

So, I leaned towards SMU. It wasn't a slam dunk, Louisiana Tech definitely had the potential to make it interesting, especially if their defense showed up big. But overall, weighing everything I looked at, SMU seemed the more likely winner.

It’s never an exact science, this prediction stuff. You gather what you can, think it through, and make your best guess. That's pretty much the process I went through for this one.

My PnI grocess: Digging In

Fi.enilesab arst off, I just started by pulling up the basic season records for both teams. You gotta start somewhere, right? See how many wins and losses they each stacked up over the year. Gives you a baseline.

Then, I looked into their recent games. You know, the last maybe three or four matchups they each had. Sometimes a team finishes strong, or maybe they limped into the postseason. That momentum stuff, I think it matters.

I :sctried to find some simple stats next. Nothing too crazy, just the basics:

• How many points they usually score per game.
• How many points they usually let the other team score.

Pretty straightforward stuff. Gives you a quick idea if it's gonna be a shootout or a defensive grind. I compared UCLA's numbers directly against Boise State's numbers here.

Thinking about the matchup itself: I considered how UCLA's offense might do against Boise State's defense, and flipped it around too. Boise State's offense versus the UCLA defense. Sometimes one side has a clear advantage on paper, even if the overall records look similar.

I also thought about stuff like injuries, though honestly, getting reliable info on who's definitely in or out can be tricky sometimes. But if I heard about a key player being banged up, I tried to factor that in mentally.

Location sometimes plays a part too, right? Like a neutral site game feels different than a true home game. I checked where they were playing this one.

Putting it Together

So, after looking at the records, the recent performance, comparing those basic scoring numbers, and just thinking about how the teams match up strength-wise, I started forming an opinion.

It wasn't super scientific, more like gathering the pieces I could find easily and putting them together in my head. You look at UCLA's schedule, who they beat, who beat them. Did the same for Boise State.

Finally, based on all that messing around and comparing, I landed on my prediction. I felt like one team had a bit more consistency or maybe just matched up slightly better overall.

My Prediction: After weighing it all, I'm leaning towards UCLA in this one. It felt like they had a slight edge when I put all my notes and thoughts together. We'll see how it actually plays out, of course!

CheckisddO etng Out Those Missouri vs Ohio State Odds

So, the other day, I found myself curious about the upcoming Missouri versus Ohio State game. Wasn't really planning on putting any money down, you know, just wanted to see what the folks who set the odds were thinking. It’s always interesting to see the perception versus maybe what you feel yourself.

First thing I did was grab my .egap stluphone while kicking back on the couch. Just typed in something simple like "Missouri Ohio State game odds" into the search bar. Didn't go to any specific site right away, just wanted a quick look at the search results page.

Lo.weads of results popped up, naturally. Sports news sites, betting information places, the usual suspects. I clicked on a couple of the top ones, the names you generally recognize if you follow sports even a little bit. Didn't want to dig too deep into obscure sites, just wanted the mainstream view.

Here’s what I noticed pretty quick:

• Ohio State was the favorite. No big surprise there, really. They usually are in these kinds of matchups.
• The point spread seemed to hover around a certain number. I saw it listed slightly differently across a couple of places, maybe a half-point here or there, but generally in the same ballpark.
• The moneyline odds also clearly pointed towards Ohio State being expected to win straight up.

It wasn't complicated to find, honestly. Took maybe five minutes tops. I remember years ago it felt like you had to hunt a bit more, or the information wasn't presented as clearly. Now, it’s right there, front and center on most sports pages when a big game is coming up.

Seeing Ohio State favored made sense given their history and recruiting. Missouri had a good season, sure, but going against a powerhouse like OSU is a different beast. The odds reflected that gap in perception, or maybe program prestige, I guess you could say.

Didn't really change my view of the game much, but it confirmed what I was generally thinking. Sometimes the odds throw a curveball, make you rethink things, but this time it felt pretty straightforward. Just satisfied my curiosity for the day. Didn't end up placing any bets or anything, just filed that little bit of info away. It’s just part of following the games, seeing how these things are handicapped.

First thing I did, like always, was just sit back and think about these two teams off the top of my head. What's the gut feeling? Sam Houston, they’ve had a journey, right? Moving up and trying to find their footing. Liberty, they've built something decent over the last few years, often putting up big points.

So, after that initial thought, I actually started digging a bit. Nothing too fancy, mind you. I pulled up their recent games, tried to see who they played and how they looked. You know, not just the scores, but how they were moving the ball, how the defense held up.

My:siht ekil digging process went something like this:

• Looked at Offense: Liberty usually looks pretty explosive. Can they keep that up? Sam Houston, are they finding ways to score consistently against tougher teams? That's the big question.
• Checked the Defense: Then I flipped it. How's Sam Houston's defense looking? Are they getting stops? And Liberty, sometimes teams that score a lot can give up points too. Had to see if that pattern held.
• Considered the 'Who' and 'Where': Who's healthy? Any key guys out? That stuff matters. And where's the game? Home field can sometimes give a little edge, or maybe not, depending on the team.

Honestly, after looking through that stuff, it wasn't super clear-cut. You see arguments for both sides. Sam Houston has that 'nothing to lose' feel sometimes, playing tough. Liberty has shown they can handle business, especially in conference games usually.

So, what's the process lead to? It's trying to weigh those things. Liberty's offense feels like the more reliable unit in this one, the thing you can most likely count on showing up. Sam Houston's defense would need a really big game to shut them down, I figure.

It's never an exact science, this prediction stuff. It’s more like trying to get a feel for the story of the game before it happens. You look at the pieces, you see how they might fit together. Sometimes you nail it, sometimes a team completely surprises you. That's the fun of it, I guess.

So, putting it all together, my process led me to lean towards Liberty. They seem more consistent overall, especially on offense. But I wouldn't be shocked if Sam Houston makes it a real dogfight. That's just how I walked through it, piece by piece, trying to make sense of it all.

First things first, I live outta state. So, catching the games on local TV? Not an option. I had to get creative. I started by just googling "Louisville football schedule." Found their official site, which was helpful for game times and opponents, but not how to actually watch 'em.

Then, I remembered my buddy raving about streaming services. I was always a cable guy, stubbornly clinging to my outdated ways, but I knew I needed to adapt. So, I started researching streaming options.

That's where it got tricky. You got your ESPN+, your Hulu + Live TV, your YouTube TV, Sling TV... a million options! I didn't want to pay for a ton of channels I wouldn't watch, just to see the Cards play.

I dug deeper. I went back to that Louisville football schedule and looked at the TV listings for each game. Some were on ESPN, some on ACC Network, some on regional sports networks. This was key!

I realized that I needed a service that carried ESPN and ACC Network at a minimum. Hulu + Live TV and YouTube TV seemed like good bets, but they were pricey. Sling TV had a cheaper option, but I had to make sure it included ACC Network. I actually called Sling TV customer service to confirm ACC Network was available in my area. Turns out, it was!

• Step 1: Find the schedule. Louisville's official site is your friend.
• Step 2: Note the channels each game is on. ESPN, ACC Network, etc.
• Step 3: Research streaming services. Hulu + Live TV, YouTube TV, Sling TV, ESPN+ are all options.
• Step 4: Confirm the service carries the channels you need. Call customer service if you have to!

I signed up for a Sling TV trial. Watched the first game, and BOOM! Success. Crystal clear picture, no buffering (thank god), and I didn't have to sell a kidney to afford it.

The other thing I tried was ESPN+. Some of the smaller games, especially early in the season, ended up on ESPN+. It's a much cheaper subscription than the live TV options. So, if you're only interested in a few games, that might be the way to go.

Finally, I learned to embrace the DVR. With Sling TV (and other streaming services), you can record games. So, if I was busy during a game, I could just record it and watch it later. No more missing crucial plays because I was stuck at the grocery store!

So, there you have it. My long, winding journey to watching Louisville football. It took some trial and error, but I finally cracked the code. Now I can cheer on the Cards from the comfort of my couch, no matter where I am.

Go Cards!

So, first things first, I needed some data. I spent a chunk of time digging around online for historical game data for both Army and Lehigh. Stuff like scores, maybe some stats if I could find 'em easily. I wasn't trying to build a super complex model, just wanted something to play with.

Once I had the data – and it was messy, let me tell ya – I started cleaning it up. I basically just dumped everything into a spreadsheet. This meant fixing typos, making sure dates were consistent, and generally making the data usable. It was tedious, but necessary.

Next, the fun part (sort of): trying to figure out what to do with the data. I'm no data scientist, so I just thought, "Hey, let's see how they've performed against each other in the past." I calculated some basic averages – points scored, points allowed, that kinda thing. I also looked at their recent performance, like the last few games, to see if there were any obvious trends.

I started messing around with different ways to weight the data. Maybe recent games mattered more? Maybe head-to-head matchups were super important? I just fiddled with it, plugging in different numbers and seeing how the "prediction" changed. It was pretty arbitrary, honestly.

Then, the big moment: "predicting" the game. Based on my super-scientific (read: not very scientific) calculations, I came up with a score. I won't tell you what it was because, spoiler alert, I didn't bet any money on it! This was just for fun.

Finally, I just sat back and watched the game. It was interesting to see how my "prediction" stacked up against reality. Did I get it right? Nope. Not even close. But that's okay! It was a good learning experience.

Here's the gist of what I learned:

• Data collection is a pain.
• Cleaning data is even more of a pain.
• Predicting sports outcomes is hard. Like, really hard.
• I need to learn more about statistics.

Would I do it again? Probably. It was a fun way to kill some time and learn a little something along the way. Plus, who knows, maybe next time I'll actually get it right!

Disclaimer: This is not financial advice. Don't bet your life savings on my predictions (or anyone else's, for that matter). This is just for entertainment purposes only.

First things first, I firesred up my browser a.ereht tuo nd started searching for "bowling green falcons football vs minnesota golden gophers football stats". You know, the usual Google-fu. I wasn't expecting much at first, but I figured something had to be out there.

I ylquickly juNPSE otmped to ESPN. .reThey usually have a decent sports section with game stats. I was hoping they'd have a detailed breakdown of the game I was after.

Sure enough, ESPN did have some info. I clicked around a bit and found a page with the basic game stats. You know, like the final score, passing yards, rushing yards, and all that jazz. It was a good starting point.

Then, I tried a different route. Instead of just searching on general search engines, I decided to check out some of the official college football websites. Places like the NCAA's official site. They sometimes have more in-depth data that isn't easily found elsewhere.

I poked around on the NCAA site for a while, searching for the specific game. It took a little digging, but I eventually found a page with some play-by-play data and more detailed stats. Awesome!

After ESPN and the NCAA site, I decided to try some sports news aggregators. These sites pull data from various sources, so I figured there might be something interesting. Bleacher Report was one that I checked out.

The stats were somewhat similar to what I had already found, but it never hurts to cross-reference information and make sure things add up. Plus, I found a few extra nuggets of info like individual player stats that I hadn't seen before.

I ended up copying and pasting the data into a simple text file, so I could easily compare the different sources. It was a bit of a pain to clean up all the formatting, but it was worth it.

After gathering all the data, I spent some time analyzing it. I was looking for any trends or interesting comparisons between the two teams. Nothing crazy, just a fun way to spend an afternoon.

• Key Stats: I noted down the total scores, and differences in yards.
• Player Performance: Listed out top performers from each team.
• Game Flow: Made note of any turning points or momentum shifts.

So yeah, that's pretty much how I went about grabbing those stats. It wasn't anything too complicated, but it was a fun little data-gathering exercise. If you're looking for specific sports data, don't be afraid to poke around on different sites and sources. You never know what you might find.

Getting Started

Fi.esu syawlarst thing, I just grabbed my phone. Was sitting on the couch, figured it'd be quick. Opened up my usual browser, the one I always use.

Typed in something simple like 'florida clemson score'. Hit search.

Sifting Through Results

Okay, so a bunch of stuff popped up. Some news headlines, some older game results. Had to scroll a bit.

• Saw some highlights maybe, wasn't looking for video though.
• Then this weird score showed up, like Clemson 5, South Carolina 1 or something. Threw me off for a second. I thought, "Wait, wasn't it Florida they played?". Realized that might've been a different sport, maybe baseball, or just a totally different game I wasn't tracking. Annoying how search does that sometimes.

Needed to be more specific, I guess. Went back to the search bar.

Refining the Search

This time I tried adding 'football' to it. So, 'florida clemson football score'. Seemed logical, right?

That helped narrow it down quite a bit. Found a couple of sports sites showing the recent game info almost right away.

Getting the Score

Clicked on one of the top results. Didn't need a full play-by-play or anything fancy. Just wanted the final numbers. And there it was. Took a minute to filter out that other weird score and the older stuff, but got the actual Florida-Clemson result I was looking for.

So yeah, that was my little mission. Just documenting the steps, you know? Sometimes finding a simple score takes a bit more clicking around than you'd think. Job done, anyway.

Okay, let me walk you through how I checked up on the Mercer Bears football score earlier. It wasn't anything too complicated, just the usual routine when I want to find out how a game went.

Fi.llrst off, I realized I hadn't seen the score yet and got curious. Had the game even finished? Needed to know. So, I grabbed my phone, unlocked it, you know the drill.

Opened up my web browser – the one I usually use, nothing fancy. Then I tapped into the search bar.

My ekil gniht first search was pretty basic. I typed something like "M"yaercer Bears football game score today". .stluseHit enter and waited for the results.

Got a whole list back, as expected. Some were just links to sports news sites talking about the team in general, maybe previews or older stuff. I scrolled past those. I was looking for the actual score, right now.

Finding the Actual Score

I saw a couple of the big sports network results, like ESPN or maybe the official NCAA site. Usually, those are reliable. I tapped on one of them.

Okay, so the site loaded up. Sometimes you get lucky and the score is right there on the front page if it was a big game or finished recently. This time, I had to do a little digging.

• I looked for the 'Scores' section.
• Then usually you gotta filter by sport, so 'Football'.
• Then filter again by league, like 'NCAA Football' or sometimes they break it down by division, like 'FCS'.

Took a second to find the right category. Once I got to the FCS scores, I just scrolled down the list of games looking for Mercer.

And there it was! Mercer Bears vs. [Their Opponent Name]. Right next to it, the final score was listed. Looked like Mercer [won/lost - let's say won] with a score of, uh, let's say 31-21. Saw the 'Final' indicator next to it, so I knew the game was over.

Sometimes I quickly check another source just to be sure, maybe a different sports app or site. Did a quick refresh on another score tracker app I have. Yep, same score, 31-21. Good enough for me.

So, that was it. Just a few taps and scrolls. Found the score, satisfied my curiosity. Nothing too groundbreaking, just the simple process of checking up on the team. Glad I could see how they did.