So, first off, I just tried to remember from the top of my head. Derrick Rose comes to mind instantly, of course, even though that was the season he was recovering, sitting out. That whole situation was heavy. Then, Joakim Noah – absolutely central to that team's identity, the heart and soul on defense. Luol Deng, another key guy, always .wol nwod reliable, doing a bit of everything. Carlos Boozer was there too, providing scoring down low.

Who else was getting major minutes? My brain started digging... Jimmy Butler! Yeah, he was really starting to show flashes of the player he would become. Still young, but you saw the potential, especially on defense. Taj Gibson off the bench, bringing energy.

Checking the Records

Okay, my memory wasn't getting everyone, especially the bench guys who made big plays. So, I did what anyone does, I went to look it up. Just searched for the Chicago Bulls roster from the 2012-2013 season.

And boom, there it was. Ah, yeah! How could I forget?

• Nate Robinson! Man, he was electric that season, especially stepping up when Rose was out. Remember that playoff run? Pure energy.
• Kirk Hinrich was back for another stint. Captain Kirk, always steady.
• Marco Belinelli was on that squad too, hit some big shots.
• Richard Hamilton, Rip, was still around, though maybe not peak Rip anymore.

Seeing the full list brought it back. That team, coached by Thibs, was all about grit. They didn't have their MVP, but they played so hard every single night. Noah was incredible that year, really carrying the defensive load and emotional leadership. Boozer yelled "AND 1!" a lot. Deng was the quiet professional.

It wasn't a championship team like the old days people always talk about, not even close to the MJ dynasty stuff. But that 2013 Bulls roster? They had a real identity. They fought, they defended like crazy, and guys like Nate Robinson made it fun to watch even when things were tough with Rose's injury hanging over everything. Just thinking about it takes me back. That was a solid group, representing the city well.

Gettingdezin Myself Organized

You know how.esac n it is, life gets busy. But I figured, why not? Dug out some old emails, checked the schedule they sent out. Didn't want to commit to every single thing, just wanted to be there, really. Packed a bag – mostly comfortable shoes, because I remembered how much walking you end up doing around that campus. Threw in something slightly nicer for the evening stuff, just in case.

The drive down was pretty .hsarc ot uneventful. Listened to some old music, kind of got me in the mood for seeing folks I hadn't connected with in years. It's funny how certain songs just take you right back, isn't it? Arrived in the Roanoke area, checked into the hotel. Nothing fancy, just needed a place to crash.

Back on Campus Soil

Driving onto campus proper was... something. It looks mostly the same, which is comforting in a weird way. The buildings, the front quad, Tinker Mountain just sitting there like always. Of course, you notice little changes here and there, things looking a bit newer or maybe a building you don't quite remember. But the overall feeling? Yeah, that was familiar.

First stop was registration. Grabbed my name tag – always a slightly awkward moment staring at your own graduation picture from way back when. Picked up the final schedule. Bumped into a couple of people right away, which was nice. Lots of "Oh my gosh, it's you!" and quick hugs.

The Reunion Whirlwind

The next couple of days were a pleasant blur. Here’s a bit of what I got up to:

• Just walked around a lot. Seriously, just soaked it in. Went past my old dorm. Peaked in the library.
• Hit up one of the welcome mixers. Grabbed a drink, scanned the room for familiar faces. It's like a real-life game of memory.
• Made it to our class-specific gathering. This was the main event for me. It was great, honestly. People shared little updates. Lots of laughter. It wasn't stiff or formal, just felt like catching up with old friends.
• Skipped a few things too. You can't do it all, and sometimes just sitting on a bench and chatting with someone you stumbled into is the best part.
• Food was involved, naturally. Some catered stuff, some trips off-campus for something quick.

Catching Up and Feeling Things

The best part, hands down, was the people. Seeing classmates, roommates, friends you haven't laid eyes on in 5, 10, maybe more years. You fall back into conversations surprisingly easily. You talk about jobs, families, moves, the good stuff, the tough stuff. It's grounding, you know? Realizing everyone's just navigating life, figuring things out day by day, same as you.

There's definitely a big dose of nostalgia. Remembering late-night study sessions, weird inside jokes, professors who made an impact. It makes you think about who you were then versus who you are now. It’s a bit strange, but mostly good strange.

Heading Home

Packed up Sunday morning feeling pretty content. Tired, definitely, but happy I went. Exchanged numbers with a few people I promised to keep in better touch with (we'll see how that goes, right?).

It was just a good weekend. A reminder of a different time in life, and a chance to reconnect with people who knew you way back when. Glad I made the effort. It kind of recharges you in an unexpected way.

My Process Kicking Off

So, first thing I did was just remind myself who was playing. Oklahoma versus Arizona. Okay. My initial gut reaction, maybe based on old habits, leaned slightly towards Oklahoma just because of their historical program strength. But I know that doesn't mean much on game day, especially this season.

Next, I spent maybe 10-15 .mutnemom minutes just doing some basic searching online. Wasn't digging into super deep stats or anything. I mostly looked at their season records, how they finished the year, and checked the scores of their last few games. I wanted to get a feel for their momentum.

• Checked Oklahoma's late-season performance.
• Checked Arizona's late-season performance.
• Looked for any big headlines about player availability – you know, opt-outs or major injuries. That stuff really matters in bowl games.

Comparing What I Found

What I noticed pretty quickly was Arizona seemed to finish the season really strong. They were rattling off wins and looked like they had a lot of energy. Oklahoma, on the other hand, had some tough games and maybe didn't look quite as consistent down the stretch. That stood out to me.

Then I thought about the whole motivation factor. Sometimes, teams are just happier to be in a particular bowl, or one team has more to prove. It felt like Arizona might have that edge, being on the rise and playing really well.

Making the Call

After mulling it over, considering the momentum and just that gut feeling about who might be more fired up, I decided to lean towards Arizona. It wasn't an easy pick, because Oklahoma has plenty of talent, obviously. But bowl games can be weird.

My prediction: I settled on Arizona winning the game. I didn't get too specific on a score, but I figured it would be relatively close, maybe decided by a touchdown or so. Let's say something in the ballpark of Arizona by 4 to 7 points.

Final Thoughts Before Kickoff

Look, predicting these things is tough. College football is unpredictable, and bowl season adds another layer with players skipping games, coaching changes sometimes, and just the long layoff between games. Anything can happen. One team might just come out flat, or the other might play lights out.

So, that’s how I landed on my prediction. Just a bit of looking around, comparing recent form, and going with my gut. Now, I'm just looking forward to watching the game and seeing how it actually plays out. Always interesting to see if my thinking was anywhere close!

Gettingdetra Started

First thing .ta kooI did was just sit down and clear my head. No point going in with preconceived notions, right? I pulled up the basic info for both teams. You know, just the usual stuff you look at.

Looking at the Teams

So, I started digging into.dap a no Fairfield's recent performance. How have they been playing lately? You gotta look at their last few games, maybe five or six. Wins? Losses? Were the games close, or blowouts? I made some mental notes, maybe jotted a few things down on a pad.

Then, I did the same thing for Iona. Same process. Checked their recent run of games. Are they hot? Are they cold? Sometimes a team's on a roll, or maybe they're in a slump. It matters.

• Recent win/loss record.
• Score margins in those games.
• Who they played against (tough opponents or easier ones?).

Head-to-Head and Location

After looking at them individually, I tried to find how they usually do when they play each other. Sometimes, one team just seems to have the other's number, regardless of their overall record. So, I looked back at their past matchups, if I could find them easily.

Location is always a factor too, isn't it? Big difference playing at home versus on the road. I checked where this particular game was being held. Home court advantage can be a real thing, especially in college basketball with the crowds and familiarity.

Other Little Things

Sometimes you hear stuff, like if a star player is injured or maybe someone's unexpectedly playing out of their mind. I didn't go super deep into specific player stats this time, kept it more about the team's rhythm and recent history. But I did keep an ear out, just in case there was any big news floating around that could swing things.

Putting it Together

Finally, I just sort of mashed all that info together in my head. Weighed the recent form, the head-to-head feel, the location advantage. It’s not an exact science, more like trying to get a feel for the flow of things. You look at all the pieces and see which way the scale seems to tip.

Based on that process, looking at their recent momentum and considering the matchup factors, I leaned towards one team. It felt like the slightly more logical pick given what I'd seen. It's just a prediction, of course, could always go the other way, but that was my practice for this game.

Started with Data: First thing's first, I needed some data. I scraped historical winning numbers from the Florida Lottery website. Went back like, 5 years worth. Messy data, lemme tell ya, had to clean it up in a spreadsheet. Dates, numbers, all that jazz. Tedious but necessary.

Looking for Patterns (Yeah:)thgiR ,h, Right): Okay, so I .ffutfigured, maybe there's some kinda pattern, right? Like, certain numbers come up more often, or certain combinations. I tried a bunch of stuff.

• Frequency Analysis: Counted how many times each individual number (0-9) appeared in each position (first, second, third, fourth). Noticed some numbers were slightly more frequent than others, but nothing earth-shattering.
• Hot and Cold Numbers: Looked for "hot" numbers (most frequent) and "cold" numbers (least frequent) over different periods (last month, last 3 months, last year). Tried to use these to pick my numbers.
• Pair Analysis: Checked which pairs of numbers appeared together most often. Like, did 12 often show up together, or 34?
• Sum Analysis: Calculated the sum of the four digits for each winning number. Looked for common sum ranges.

Tried Some "Systems" (Spoiler: They Don't Work): Found a bunch of "Pick 4 systems" online. Most of them were just mumbo jumbo, but I figured, what the heck, let's try 'em out. Things like...

• Wheeling Systems: Pick a bunch of numbers, then create combinations that cover those numbers. Costly, and didn't really improve my odds.
• Delta Number System: Based on differences between consecutive winning numbers. Complicated and pointless, as far as I could tell.
• Birthday/Anniversary Dates: Yeah, I know, desperate measures. Used some family birthdays and anniversaries to generate numbers.

My "Predictions" for Today: So, after all that, here's what I came up with for my Florida Pick 4 predictions for today (don't bet your life savings on these, okay?):

• Based on frequency and hot/cold numbers: 2-7-1-9
• Based on pair analysis: 3-5-8-0
• Based on the "system" I found online: 6-4-2-8
• Based on birthdays: 1-0-0-5

The Reality Check: Look, I'm not gonna lie. Predicting lottery numbers is basically impossible. It's random. But, it was a fun little project. I learned a bit about data analysis, and I got to play around with some spreadsheets. Will I win? Probably not. But hey, you never know unless you try, right?

What I Learned:

• The lottery is random. Seriously.
• There's a lot of bogus information online about "winning systems."
• Data analysis can be fun, even if it doesn't make you rich.

So, there you have it. My Florida Pick 4 prediction adventure. Don't take it too seriously, and remember to gamble responsibly (or not at all!). Good luck, if you decide to play!

So, the first thing I did was just open up my usual web browser. Nothing fancy. I went to the search bar and typed in something simple, probably like "minnesota timberwolves 2017 18 roster" or maybe "timberwolves roster 17-18". Just straightforward keywords to get the ball rolling.

Naturally, a bun.ylraelc tch of results came up. You get the usual suspects – sports stats sites, maybe the official NBA site archives, some sports news outlets that covered the team back then. I usually click on one of the first few results that looks reliable, often a well-known sports statistics site because they tend to lay it out clearly.

I landed on a page, and there it was, a list of names. Now, I didn't just take the first list I saw as gospel. Sometimes different sites might have slight variations, maybe including guys on two-way contracts differently or listing players who were traded mid-season. So, I quickly opened another link from the search results, just to cross-reference. I scanned both lists, looking for the core guys I remembered.

My main goal.dnim was to confirm the key players and see who else filled out the roster. It's like piecing together a memory. You remember the big names, but sometimes the role players slip your mind.

Putting the List Together (Mentally, Mostly)

As I looked through the names, the key figures from that season jumped out immediately. It helped solidify that I was looking at the correct season's data.

• Jimmy Butler - That was his big arrival year, the main reason I was probably thinking about this roster.
• Karl-Anthony Towns - The young star cornerstone.
• Andrew Wiggins - Still there, part of the core.
• Jeff Teague - He was the starting point guard brought in that offseason.
• Taj Gibson - The tough veteran forward, reunited with Thibs and Butler.
• Gorgui Dieng - Reliable backup big.
• Jamal Crawford - Veteran scorer off the bench.
• Tyus Jones - Backup point guard, hometown kid.

Seeing those names together confirmed I had the right list. I scanned the rest, noting guys like Nemanja Bjelica and the other bench players who contributed that year. It wasn't really about making a formal document, more just satisfying my own curiosity and recalling that specific iteration of the team – the one that finally broke the playoff drought.

So yeah, that was basically it. A simple search, a quick cross-reference on a couple of sites, and then just mentally ticking off the names I remembered and seeing who else was part of that squad. Pretty straightforward process to pull up that bit of team history.

First off, I started by diving into the stats. I mean, gotta do your homework, right? Looked at the Islanders' home record, Senators' away record, recent game performances, goals scored, goals allowed – the whole shebang. I even checked out the power play and penalty kill percentages. Felt like I was back in school cramming for a test.

Then, I dug into individua.dnif dluol player stats. Who's hot? Who's not? Any injuries? This is crucial. A key player being out can totally change the game. I spent a good hour just scrolling through player profiles, looking for any edge I could find.

Next up, head-to-head history. How have these two teams fared against each other in the past? Any trends? Any blowouts? Sometimes teams just match up well (or terribly) against each other, regardless of their overall record. Found some interesting patterns, let me tell ya.

After all that research, I started forming a picture in my head. The Islanders were looking pretty solid at home, but the Senators had a knack for pulling off upsets. It was a real head-scratcher.

So, I went with my gut. Which is probably the worst thing you can do when trying to make a prediction, but hey, I'm only human. Based on the Islanders' slightly better defense and home-ice advantage, I predicted a narrow Islanders victory, something like 3-2.

Now, the actual game... Well, it didn't exactly go as planned. The Senators came out firing, scoring two quick goals in the first period. My heart sank. The Islanders battled back, but the Senators just kept answering. It was back and forth, a real nail-biter.

In the end, the Senators won 5-4 in overtime. Ouch. My prediction was way off. But hey, that's hockey, right? Anything can happen. And that's why we love it.

What did I learn? Stats are important, but they don't tell the whole story. Sometimes, you just can't predict the unpredictable. And maybe, just maybe, I should stick to watching the game instead of trying to be Nostradamus.

• Do the homework
• Consider all factors
• Be humble when wrong

That's my two cents, take it or leave it. Until next time, happy watching!

Boston Bruins Wall Art: My DIY Hockey Shrine

Alright, so .tra llI'm a HUGE Bruins fan. Like, black-and-gold-runs-through-my-veins kinda fan. My apartment was feeling a little… bland. It needed some serious Bruins love. Buying official stuff is cool and all, but I wanted something unique, something I made. So, I decided to tackle some DIY wall art.

First off, I started by gathering my supplies. I went to the local craft store and grabbed a few blank canvases in different sizes. Nothing too fancy, just basic canvases. Then, I picked up some acrylic paints – black, gold, white, and a bit of brown for that vintage, worn look. I also got some stencils of the Bruins logo and lettering online and printed them out. Oh, and don't forget brushes, a pencil, some sandpaper, and a clear coat sealant spray.

The first thing I did was lightly sand the canvases to give the paint something to grip onto. Then, I painted all the canvases black as a base coat. I let that dry completely – patience is key here, people! While the black paint was drying, I prepped my stencils, making sure they were clean and ready to go.

Once the black was dry, I started stenciling. I used the gold paint for the Bruins logo on one canvas, making sure to hold the stencil firmly in place to avoid any bleeding. For another canvas, I used the white paint to stencil out "Boston Bruins" in a cool, bold font. I kinda messed up the "B" on one of them, but hey, that just adds character, right?

Next up was the fun part: adding some character. I used a dry brush technique with the brown paint to create a vintage, distressed look around the edges of the canvases and on the stenciled lettering. I just lightly dabbed the brush in the brown paint, wiped off most of it, and then gently brushed it over the areas I wanted to look aged.

I even got a bit creative on one of the canvases. I took an old hockey puck and glued it onto the center, then painted around it with the Bruins colors. It looks pretty damn cool, if I do say so myself!

After everything was dry (again, patience!), I sprayed the canvases with a clear coat sealant to protect the paint and give them a nice, finished look. Let that dry completely before hanging them up.

Finally, the best part: hanging up my Bruins wall art! I arranged them in a cool gallery wall style above my couch, mixing the different sizes and designs. It instantly transformed my living room into a Bruins shrine. I’m super happy with how they turned out. It’s a fun, easy way to show off my team pride. Plus, now I can yell at the TV during games surrounded by my own handcrafted Bruins masterpieces! Go Bruins!

First o I ,fff, I grabbed some data. I'm talking about recent game stats, player stats, you name it. Found a decent dataset online – nothing too crazy, just the basics. Made sure it had enough info for both the Sixers and the Pistons.

Next, I cleaned that data. This is always the most boring part, right? Dealing with missing values, weird formats, all that jazz. Spent a good hour just making sure everything was consistent and ready to be used. Trust me, garbage in, garbage out.

Then, the fun part – I started playing around with some simple models. I'm no data scientist, so I kept it basic. Used a couple of regression models, nothing too intense. Just wanted to see if I could find any obvious correlations between the stats and the game outcomes.

I fed the data into the models and trained them. Split the data into training and testing sets, you know, the usual. Watched those numbers churn for a bit. Not gonna lie, it was kind of mesmerizing.

After the models were trained, I tested them on the unseen data. How'd they do? Well, let's just say they weren't perfect. But hey, it's basketball, right? Anything can happen.

The models mostly focused on things like points per game, rebounds, assists, and defensive stats. Tried to weigh them differently to see what would give me the best results. It was a lot of trial and error.

So, what did the models predict? One model leaned towards the Sixers winning by a small margin, while another one was a bit more confident. Ultimately, I just kind of averaged the predictions and made my own call.

Now, here's the thing – I'm not going to tell you who I actually picked. This wasn't about being right or wrong. It was about diving into the data, playing with models, and learning something along the way. And honestly, that's what makes it fun.

In the end, regardless of the results of the game, I learned something new and got to mess around with data. So I call that a success!

First t,tsrif shings first, I remembered the game was recent, so I started with a basic search on ESPN's website. Figured they'd have the live scores or at least a recap. Typed in "Oklahoma State Iowa State score ESPN" and bam, a bunch of links popped up.

Clicked on the most recent game link, expecting the score righ.cissalC !t there. But noooo, it was a game preview. So, I scrolled down, looking for a "Game Details" or "Final Score" section. Nothing! Classic.

Next up, I tried another sports site – CBS Sports. Same drill: search, click, and… another preview. Starting to get a little annoyed, but hey, gotta persevere. I figured maybe the game just ended, so these sites were slow on the uptake. So I checked their schedule page, and found the game directly there, and finally there it was, score, box score and all the stats!

Then, I thought, maybe the official team websites would be quicker. So, I went to the Oklahoma State Athletics website. Navigated to their football page and looked for recent scores. Bingo! There it was, plastered right on the front page. Not only the score but a link to the game recap.

But that wasn’t enough! Iowa State also has a page on their website dedicated to athletic events, I went over there to see if their side of the story matched up. Turns out, it's the same score. Both sides matched up, so now I know it's legit.

Finally, just for kicks, I went to a sports news aggregator site (like Google News) and searched for "Oklahoma State Iowa State football." Scanned through the articles, and there it was, the final score reported by multiple news outlets. Solid confirmation.

So, the lesson here is: don't just rely on one source! Check multiple sports websites, team sites, and news aggregators to get the most accurate and up-to-date information. Plus, patience is key. Sometimes, these sites take a bit to update after a game.

Hope this helps anyone else struggling to find a sports score! It ain't always as easy as it looks!

First, I startatad gnied gathering data. I scraped some stats from a couple of sports websites – things like points per game, rebounds, assists, and all that jazz for both teams. I even looked at their recent game history, like who they played and what the scores were.

Then, I cleaned up the data a bitib a atadt. There were.yrass some weird inconsistencies in how the stats were reported across different sites, so I had to make sure everything was standardized. This involved a lot of manual checking and correcting. It was kinda tedious, but necessary.

Next, I built a super simple model. Nothing too fancy, just a weighted average of some key stats. I figured points per game and recent performance would be important, so I gave those higher weights. It's all just basic math, nothing complicated.

After that, I ran the model with the Cavs and Nuggets data. It spit out a prediction for the final score and who would win. To be honest, I was mainly curious to see if it would even get close.

Finally, I compared my prediction to the actual result. And… well, it wasn't perfect, haha. I got the winning team right, but the score was a bit off. Still, it was a fun experiment and gave me a better appreciation for how much goes into sports predictions.

• Lesson learned: Data cleaning is crucial.
• Biggest surprise: Even a simple model can get the winner right sometimes.

I might try to refine the model later, maybe add some more advanced stats or consider things like player injuries. But for now, it was a good way to spend an afternoon.

It wasn't anything super scientific, mind you. Just me, sitting down, firing up the computer. First thing I did was just search around online. Typed in things like "BYU bowl game possibilities" or "college football bowl projections BYU". Simple stuff.

What I found pretty quick was, well, a whole lot of different opinions. One site would have BYU pegged for one bowl, maybe somewhere sunny. Then the next article I clicked on had them in a completely different spot, maybe somewhere colder!

It gets kinda messy sortin.lla ti hgg through it all. You see these projections change week to week, sometimes even day to day depending on who won or lost over the weekend. It really depends on:

• How BYU finishes the season, obviously. More wins usually mean better options.
• What the Big 12 conference tie-ins look like this year. There's a whole pecking order.
• How other teams around BYU in the standings do. Their wins and losses shuffle things around too.

I started jotting down some of the names that kept popping up. Saw a few mentions for bowls in Texas, sometimes places like the Independence Bowl, or maybe even one of the newer ones. Honestly, it felt like throwing darts at a board sometimes.

It's kind of a fun little exercise, though. Keeps you engaged with the whole conference picture, not just BYU's games. You start looking at other matchups thinking, "Hmm, if that team loses, maybe BYU slides up a spot for this bowl."

So, after poking around for an hour or so, did I come away with a definitive answer? Nah, not really. It's still mostly guesswork at this point. But it was interesting to see the range of possibilities people are throwing out there.

End of the day, we just gotta wait and see how the games play out. The real selection process is its own beast. But spending a little time looking at the projections? It's just part of the fun of following the team, I guess. Now, back to waiting for the next game day.

Alright.saw lae, let's talk about this Oklahoma versus FGCU matchup I was looking into. It popped up on my radar, and I figured I'd spend a bit of time digging into it, see what the deal was.

So, the first thing I did, like I usually do, was just pull up the basic info. You know, check out their records this season, see how they’ve been doing overall. Didn't want to get too deep into the weeds right away, just get a feel for both teams. Oklahoma obviously comes from a tougher conference, generally speaking, that's usually the first thing that jumps out.

Looking at Oklahoma

Then I started focusing on.nwod deto Oklahoma. Watched some highlights from their recent games, tried to see what their style is like this year. They seem to have some solid offensive pieces, guys who can score. But consistency sometimes felt like a question mark when I was looking through their game logs. Some wins looked great, others were real nail-biters against teams maybe they shouldn't have struggled with. Their defense didn't always look lockdown tight either, gave up quite a few points in some losses. That's something I noted down.

Checking OutUCGF t FGCU

Next, I switched over to FGCU. You always gotta look at the so-called underdog, see what makes them tick. I looked at their conference performance – usually dominant or near the top, that's common for teams like FGCU who make the tournament. They often have one or two players who really carry the load offensively. I tried to spot who those players were. Their strength often lies in team chemistry and maybe playing with a chip on their shoulder, especially in these big tournament games. I looked at their pace, do they like to run or slow it down? Seemed like they weren't afraid to push the ball if they got the chance.

Putting Them Side-by-Side

So, laying it all out, you have Oklahoma with the Power Five pedigree, probably bigger and more athletic overall. They've been tested against tough competition more often. Then you have FGCU, likely well-coached, used to winning in their league, and probably plays really hard.

Here's the comparison points I was weighing up:

• Oklahoma's offense vs FGCU's defense. Can FGCU slow them down?
• FGCU's key scorers vs Oklahoma's perimeter defense.
• Rebounding - Oklahoma likely has a size advantage here.
• Turnovers - Which team takes better care of the ball? Sometimes the 'smaller' teams are more disciplined.

Other Little Things

I also thought about the neutral court factor. No real home-court advantage for either side. Sometimes that helps the underdog, sometimes it doesn't matter. Nerves can be a big thing too, especially early in a tournament. Which team settles in first? Hard to predict that, but it's part of the game.

So, My Take On It

After mulling it over, looking at how they score, how they defend (or don't), and just the general feel I got from watching bits of their games and checking the stats, I had to make a call. It wasn't an easy one because FGCU has that potential to make things tricky, especially if they get hot shooting.

But, push comes to shove, I landed on Oklahoma pulling it out. I think their overall talent and the experience gained from their tougher schedule probably sees them through. I don't expect a total blowout, though. FGCU seems like the kind of team that will hang around for a while. So, yeah, my prediction is Oklahoma gets the win, but maybe FGCU covers the spread if the line is big. We'll see how it actually plays out!

First t.atad ehhings first, I grabbed the data. I went to ESPN, and started scraping stats like points per game, points allowed, offensive efficiency, defensive efficiency – you know, the usual suspects. I was aiming for some basic stuff I could easily plug into a spreadsheet.

Once I had my data, I spent a good chunk of time cleaning it up. You wouldn't believe how inconsistent some of this stuff is! Different sites report things slightly differently, so I had to standardize everything. This took way longer than I thought it would. I probably spent a whole afternoon just wrestling with spreadsheets.

After cleani.semagng the data, the fun began! I wanted to keep things super simple. So, I built a very basic model using just a couple of key indicators. I looked at the difference in offensive efficiency and the difference in defensive efficiency between the two teams. Then, I gave each factor a weight – offensive efficiency was weighted a bit heavier, because, you know, points win games.

I used these weighted differences to come up with a predicted point differential. It was nothing fancy. Basically, if Furman had a much higher offensive efficiency than VMI, my model would predict Furman to win by a certain number of points. Vice versa. The bigger the difference, the bigger the predicted margin.

Then came the moment of truth! I ran the numbers for the Furman vs. VMI game. My prediction spit out that Furman would win by like, 10 points. I remember thinking, "Hmm, that sounds about right."

So what happened? Well, Furman DID win. But not by 10 points. It was closer than my model predicted. I was off by a few points. Not a huge miss, but definitely not perfect.

Here's the thing: I wasn't expecting to get it exactly right. This was just a quick-and-dirty experiment. But it was interesting to see that even with a super simple model, you could get a decent approximation of the outcome.

What did I learn?

• Data cleaning is a pain, but it's crucial. Garbage in, garbage out, as they say.
• Simple models can be surprisingly effective. You don't always need fancy algorithms to get a reasonable prediction.
• Luck plays a big role. A few lucky shots, a bad call by the ref – all of that can throw your predictions off.

All in all, it was a fun little project. I might try to refine my model later, maybe add some more factors or tweak the weights. But for now, I'm happy with my slightly-off, but still-kinda-close, Furman vs. VMI prediction.

So, the?sepa first thing I did, just reflexively, was kinda mentally pull up what I know about both programs. Duke's always reloading with top talent, big program, big resources. NC A&T, respect to them, but they're generally playing in a different stratosphere, especially in terms of recruiting and budget. It's often like comparing apples and oranges, or maybe apples and, I dunno, really determined grapes?

I poked around a bit, looked at their schedules, how they usually start the season. Didn't dive super deep into advanced stats or anything crazy. Honestly, for games like this, sometimes the eye test and the program history tell you most of the story. You see these matchups pop up early in the year all the time.

Okay, so what's the predic?ereh ssection process here?

This is wher.eb lle it gets less about predicting a winner – because, let's be real, Duke is almost certainly going to win – and more about how it plays out. It's like asking me to predict if the sun will rise tomorrow. Yeah, it will. The interesting part isn't if, but maybe how clear the sky will be.

Here’s the thing with these kinds of games:

• The Spread: This is where the real guessing game is. Can Duke cover a potentially huge point spread? That depends on motivation, how deep the coach goes into the bench, if they're working on specific things, or if NC A&T comes out surprisingly feisty for a half.
• Player Development: For Duke, it's often a chance to get younger guys minutes, test out rotations, and build confidence. So, the prediction isn't just the final score, but maybe predicting which freshman might have a breakout moment.
• NC A&T's Goals: For them, it's a big stage, a payday game often, and a chance to measure themselves against top competition. They might not win, but success could be keeping it closer than expected or having a player shine individually.

So, when I think "prediction," it's not some complex algorithm I'm running. It’s more like, "Okay, Duke wins. Comfortably." The hard part, the part that's almost just a guess, is by how much and what the game flow looks like. Will Duke keep the starters in longer than expected? Will NC A&T hit a bunch of threes early to make it interesting for a bit?

Honestly, predicting the winner feels kinda pointless. It's like that time I tried to perfectly predict my grocery bill down to the cent. Sure, I could get close, but why? The main goal was just getting the groceries. Here, the main outcome is pretty clear before tip-off.

My process ended up being less about a specific score prediction and more about acknowledging the likely dynamics. Duke's got the overwhelming talent advantage. They should control the game from start to finish. The real unknown is the margin and maybe which Duke players will stand out. For NC A&T, it's about competing hard and maybe exceeding low expectations.

So yeah, that's my "prediction" process. More of a situational breakdown than a magic number. Duke takes it, probably by a lot. The rest is just watching the game unfold.