DEV Community: Marina Kim(Eunji)

Why I stopped analysing matches and started caring about spectators

Marina Kim(Eunji) — Tue, 03 Feb 2026 11:05:08 +0000

The reason I stopped writing match analysis is actually very simple.

Sports don’t survive without spectators.
Without spectators, there’s no revenue.
Without revenue, players struggle to live as players.
And when that happens, leagues shrink.

The more I wrote analysis, the harder it became to ignore that reality.

I studied sports data and analysis, and for about three years I worked by writing match previews and analytical pieces.

Analysing games wasn’t a hobby for me.
It was how I learned the sport, how I thought about it, and how I contributed to it.

Analysis explains results — but spectators decide survival

Match analysis usually answers one question:

“Why did this game end the way it did?”

Why this team won.
Why that team lost.
Why the tactics worked.
What might happen next.

But over time, a different question started bothering me more:

Even if the analysis is good,
what happens if no one is actually watching the sport?

This question becomes very real when you think about sports in Korea.

The reality of non-popular sports in Korea(My country)

In Korea, only a few sports attract stable crowds and money:

Baseball
A well-known football clubs
Some volleyball teams
Basketball

Outside of those, many athletes struggle.

Not because they aren’t good enough —
but because there simply aren’t enough spectators.

For many players, being an athlete isn’t financially sustainable on its own.
They have to worry about second jobs, future careers, and life after sport
while still competing.

That’s not a performance problem.
It’s a structural one.

Watching non-popular sports in London changed my perspective

After moving to London, I started watching sports that would be considered “non-popular” back home.

And what surprised me wasn’t the level of play.
It was the crowd.

Even without big stars or TV coverage:

people showed up
families came together
locals dropped in casually
not everyone was a “fan”

Sport existed as something you could simply attend.

That contrast stuck with me.

It made me ask a different question:

Maybe the problem isn’t that people don’t care about sports.
Maybe they just don’t know what’s happening nearby.

“What if all sports events were visible in one place?”

That’s where this project started.

I wondered:

What if you didn’t need to be a fan?
What if you didn’t already follow a league or a team?
What if you could just see what’s happening today, near you?

If sports events were easier to notice,
maybe a few more people would show up.
Maybe that alone could make a difference.

So I started building a platform that aggregates sports events by location and time.

Then I discovered the uncomfortable truth

Once I started building and observing behaviour, two things became clear.

Sports fans

focus only on the sport they already love
don’t really explore other sports

People with little interest in sports

don’t explore sports at all
they don’t even open a “sports discovery” product

That’s where the contradiction appeared.

I wanted to expose sports to people who weren’t fans.
But those people don’t actively look for sports in the first place.

That’s when the product’s purpose started to break

My original goal was simple:

“Create a new entry point that brings spectators.”

But in reality:

fans don’t need discovery
non-fans don’t want discovery

Trying to force exploration didn’t work.

So the product slowly shifted.

From discovery
to confirmation.

What Sportsive is now

Today, Sportsive isn’t trying to convert people into sports fans.

It’s not trying to persuade anyone.

Instead, it’s for a much narrower moment:

When someone is already thinking
“Should I go out tonight?”
Sportsive answers
“Yes, something is happening” or “No, it’s quiet.”

It’s a checking tool.

If someone opens it for a few seconds, gets an answer, and leaves —
that’s success.

Why I really stopped analysing matches

If I had to summarise it in one sentence:

I became more interested in why spectators aren’t there
than in why a match ended the way it did.

That’s why I stopped analysing games
and started experimenting with how people even discover that a game exists.

I don’t know if this turns into a business.
I don’t know if it actually helps sports in a meaningful way.

But the problem feels real enough to explore.

If this sounds familiar to you, I’d love to hear your thoughts.

👉 https://sportsive.vercel.app

One last line

Match analysis explains sport.
Spectators sustain it.

Why “just list all sports events” is much harder than it sounds

Marina Kim(Eunji) — Mon, 26 Jan 2026 20:37:41 +0000

Sports don’t have a discovery problem. They have a structure problem.

After publishing my last post about building a small “checking tool” for sports events,
a few people reached out with the same question:

“Why hasn’t someone already solved this?”

At first glance, it does sound simple.

Sports events are public.
Schedules exist.
Teams publish fixtures.
Leagues have websites.
Ticket platforms list events.

So why is it still hard to answer a basic question like:

“Is there any sport happening near me tonight?”

The short answer is:
because sports data is structurally messy.

Sports aren’t one system. They’re thousands of exceptions.

When people talk about “sports events”, they often imagine football leagues with clean fixtures.

But once you go beyond that narrow view, everything breaks.

Some sports are league-based.
Some are tournament-based.
Some are sessions (horse racing meetings, tennis days).
Some last 90 minutes.
Some last 6 hours.
Some last two weeks.

Even within one sport, formats vary wildly.

A football match has:

a kickoff time
a predictable duration
two teams

A tennis tournament has:

no fixed match times
daily sessions
matches that depend on previous results

A horse racing event:

is technically one “meeting”
but contains many separate races
with different relevance for spectators

Trying to put all of this into a single, clean list is not trivial.
It’s not a UI problem.
It’s not an AI problem.

It’s a data modeling problem.

“Just aggregate everything” doesn’t scale

Another common suggestion is:
“Why not just scrape everything?”

This ignores three realities:

Data is fragmented by responsibility
Teams, leagues, venues, and organizers all publish different parts of the truth.
Officialness matters
Not all sources are equally reliable, and updates don’t propagate consistently.
The cost of edge cases explodes
Every new sport, country, or tournament introduces new rules, formats, and exceptions.

At small scale, manual curation works.
At large scale, blind automation breaks.

This is why big tech companies don’t fully solve this problem either.
Not because they can’t — but because it doesn’t fit their economics.

The real question isn’t “what sport do you like?”

Most existing sports products assume one thing:
that users already know what they want.

They ask:

Which team do you follow?
Which league do you care about?
Which sport are you into?

But many real-life decisions don’t start that way.

They start with:

“I have a free evening.”
“I’m bored.”
“I might go out.”

Only after that does sport become an option.

That’s why I stopped thinking in terms of teams and leagues,
and started thinking in terms of location and time.

Not as a fan.
Not as an analyst.
But as someone deciding what to do next.

I don’t think this is a product problem (yet)

I’m not convinced this is “a startup” in the traditional sense.
I don’t even know how people should pay for something like this.

What I am convinced of is this:

Sports event discovery fails not because of missing information,
but because the information is structured for organizers — not for human decisions.

Right now, I’m less interested in growth or monetization,
and more interested in understanding whether this gap is real.

Do people actually want to check sports this way?
Do they open something, get an answer, and leave?
Does location-first, time-first information change behaviour at all?

I don’t know yet.

But I do know this problem is deeper than it looks —
and that’s what makes it worth exploring.

(If you’ve ever felt this friction yourself, I’d genuinely love to hear how you deal with it.)

I stopped analysing matches and started building a tool to decide whether to go

Marina Kim(Eunji) — Sun, 25 Jan 2026 17:58:52 +0000

I used to analyse matches.

Now I’m trying to solve a different problem.

For a long time, I wrote about sports by looking backwards.

I analysed matches using data — possession, shots, goalkeeping decisions — and tried to explain why a game ended the way it did.
One of my last posts here was about the UEFA Women’s EURO final and how England trusted their goalkeeper.

I still enjoy that kind of analysis.
But at some point, I realised I was personally stuck with a much simpler, more practical problem.

Living in the UK made something obvious

I live in the UK, where sports events happen constantly.

Football, rugby, tennis, horse racing — almost every day, something is going on somewhere.
And yet, as someone who isn’t a die-hard fan of one specific team, I kept asking myself:

“Is there anything actually happening near me right now?”
“Is there a match tonight that’s worth just going to?”

Surprisingly, this was hard to answer.

Sports information is everywhere — but it’s fragmented:

team websites
league schedules
broadcasters
ticketing platforms

All of them assume you already know what you’re looking for.

What they don’t answer well is:
“Today. Now. Near here.”

I tried building a platform. That was a mistake.

When I first decided to build something, I did what many people do.

I added:

user accounts
feeds
chat rooms
team pages
meetups

On paper, it looked like a “proper platform”.

In reality, it completely missed the point.

The product became about staying, scrolling, and engaging —
when the original problem was about making a quick decision and leaving.

So I did something uncomfortable.

I deleted almost everything.

What’s left is just a map

The current version is intentionally small.

No login.
No social features.
No feeds.
No following teams or leagues.

Just:

a map
sports events shown only by location and time
simple signals like LIVE or starting soon

The idea is not to keep people inside the product.

It’s a checking tool — something you open for a few seconds to decide:

“Should I go out or not?”

If someone opens it, gets an answer, and closes it immediately — that’s success.

This is still an experiment

I don’t know if this becomes a business.
I don’t even know how people should pay for something like this yet.

Right now, I’m trying to answer a simpler question:

Do people actually find this useful?

Not fans.
Not analysts.
Just people deciding what to do with their evening.

If you’re curious, this is what the project looks like now:
👉 sportsive.vercel.app

I’m still learning, and I’m very open to feedback — especially if this sounds like a problem you’ve felt too.

[Personal Project #19] UEFA Women’s EURO 2025 Final: England Trusted Their Goalkeeper

Marina Kim(Eunji) — Mon, 28 Jul 2025 10:19:18 +0000

On July 28, 2025, in Basel, Switzerland, the UEFA Women’s Euro 2025 final was held.
England and Spain played to a 1-1 draw after extra time, and the match went to penalties.
In that dramatic moment, England trusted their goalkeeper, Hannah Hampton — and she delivered again.

England won the shootout 3-1. And this win wasn’t just luck.

1. Strategy Built from Experience – The Quarterfinal

England had already faced a penalty shootout in the quarterfinal against Sweden.
They won that game thanks to two saves by Hannah Hampton.

That match gave the team more than a win — it gave them trust in their goalkeeper.
So in the final, maybe England thought:

“If we reach penalties, we know we can win.”

And during the game, England played as if that plan was clear from the start.

2. Extra Time Decision – Defense Over Attack

In the 30 minutes of extra time, both teams were tired. But their plans were different.
England didn’t try to attack. They focused only on defending.

Stat	England	Spain
Shots	0	5
Shots on target	0	0
Expected goals	0	0.72
Clearances	16	6
Interceptions	3	2

England didn’t take a single shot.
Spain took 5 shots, but none were on target.
Hampton didn’t have to make any saves in extra time — the defense did it all.

This was not just defending. It was a smart and planned strategy.

3. Zero Mistakes? — Small Errors, Big Discipline

Stat	England	Spain
Mistakes leading to shots	1	1
Mistakes leading to goals	0	0

Both teams made one mistake that led to a shot, but neither allowed a goal from those errors.

England's defense stayed calm even after the small mistake.
More importantly, they didn’t collapse or lose shape, even under Spain’s pressure.

So while it wasn’t zero mistakes, it was still a highly disciplined performance —
the kind that wins finals not with flair, but with focus.

And again, at the center of this discipline was their trusted last line: goalkeeper Hannah Hampton.

4. Spain's Possession Game — and Its Limit

Spain came into the final with the highest average possession in the tournament: 77%.
But in the semifinal against Germany, their style didn’t work as well.
Germany’s high pressing made Spain uncomfortable. They still won, but needed extra time.
In that match, Spain’s possession dropped to 66% — a big change for them.

In the final, they had more possession again, but the pattern repeated:

Period	Spain Possession (%)
1st Half	67%
2nd Half	59%
Extra Time	71%

Even with 71% of the ball in extra time:

They didn’t hit a single shot on target.
All 5 shots were blocked or missed.

Spain showed something important:
“When their possession is broken, their whole game can fall apart.”
Possession brought them far, but goals — or good defense — win trophies.

5. Goalkeeper Performance Data and Analysis

In this final, two goalkeepers played a very important role:
Hannah Hampton (England) and Cata Coll (Spain).
When we compare their performance, we can see some interesting points.

Key stats explained:

Cmp% (Pass Completion Percentage): This shows how many passes the goalkeeper made successfully. Hampton completed 33.3% of her passes, which is much higher than Coll’s 16.7%. This means Hampton was better at passing the ball.
Launch% (Long Pass Success Rate): This is the success rate of long passes or goal kicks. Hampton’s success rate was 47.5%, which is much better than Coll’s 18.8%.
Launch%_crosses (Cross Success Rate): This shows how well the goalkeeper completed crosses (passes through the air). Hampton had a very high success rate of 94.4%, while Coll had little or no success in this area.
Stp% (Sweeper Stop Percentage): This shows how often the goalkeeper stopped the opponent’s attacks early. Hampton had a 3.6% success rate, showing she was more active in stopping attacks.

Looking at these stats, we can understand why England’s trusted goalkeeper, Hampton, played such an important role in the final.

6. Belief Became a Strategy

England didn’t give up on attacking.
They chose to trust Hampton and play for penalties.

"We have a goalkeeper we trust. And we’ve won a shootout before."

That belief led them to the top again.
The star of the final wasn’t a striker — it was the goalkeeper who kept the goal closed after the first half.

Final Thought: Defense Was a Choice — and Hampton Led It

This win wasn’t about the best shooter.
It was about trust, strategy, and experience — and Hannah Hampton.

England trusted theiar goalkeeper.
And that trust brought home the Euro 2025 trophy.

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, matplotlib, Seaborn
Data Source: Flashscore
Github: https://github.com/k-eunji/weuro_final

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

Sure! Here’s the title and table in English as you requested:

[Personal Project #18] UEFA Women’s EURO 2025 Semifinal – Spain’s Possession Shaken: Dramatic Goal at the End

Marina Kim(Eunji) — Fri, 25 Jul 2025 10:37:57 +0000

In the UEFA Women’s Euro 2025 semifinal, Spain defeated Germany 1–0 thanks to a dramatic goal in the 113th minute. But this match was more than just a win — it was a real test of Spain’s football style and identity.

1. Before the Match: Two Very Different Styles

Stat	Spain (Avg.)	Germany (Avg.)
Goals	4.0	1.5
Shots	24.5	17.0
Shots on Target	8.0	4.25
xG	3.21	2.09
Big Chances	4.0	2.75
Possession	74.3%	54.8%
Pass Accuracy	88.8%	75.8%

Spain: A team that controls the game with high possession and clean passing
Germany: A team that presses hard, defends well, and tries to disrupt the opponent’s rhythm

2. Match Day Data – A Day Spain’s Possession Was Broken

Period	Spain Possession	Shots	On Target	xG
1st Half	77%	12	5	1.90
2nd Half	63%	5	2	0.49
Extra Time	58%	5	2	0.65
Total	66%	22	9	2.23

Spain usually controls over 74% possession, but this time it dropped to 66% — a sign that they struggled to play their usual tempo.
Germany attacked more in the second half and extra time, with 13 of their 15 shots coming in that period.
Spain’s xG in the second half was only 0.49, far below their average of 3.21.

Defensive Stats of Germany

Category	1st Half	2nd Half	Extra Time	Comparison & Interpretation
Tackle Success Rate	57%	44%	64%	Similar or higher than Spain (75%, 43%, 43%), showing consistent defensive pressure
Clearances	11	13	10	Significantly more than Spain, actively clearing danger from critical areas
Interceptions	7	1	3	Effectively disrupting Spain’s passes, controlling the flow of the game
Save	5	2	1	The goalkeeper made crucial saves

👉 This match was proof that Germany successfully broke Spain’s rhythm and controlled the game on their terms.

Graph Explanation

1) Consistent shot attempts & high xG

The graph shows Spain’s cumulative xG rising quickly after around the 40th minute in the first half and again in early extra time.
This means Spain had many shots and created strong scoring chances during these times.

2) Fewer shooting chances in the middle of the second half

However, after the middle of the second half, the cumulative xG almost stopped rising, which means Spain’s shooting chances decreased.
This shows how Germany’s strong defense reduced Spain’s chances, which is why Spain scored only one goal.

3. Athenea: A Game-Changing Substitution

Player	Subbed In	Assist	Dribbles (Success)	xG Involvement	Touches
Athenea del Castillo	77th min	1	4 (1 success)	0.5	26

Athenea entered the game with just over 40 minutes left and made the key assist.
Her quick reaction and smart pass gave Aitana Bonmatí the space she needed to score the winning goal.
One moment of brilliance changed everything.

4. Why Was It So Difficult?

👉_This part reflects my own opinion and interpretation._

(1) Grass Surface – Spain Lost Their Rhythm

This was Spain’s first match on natural grass in the tournament. All previous games were on artificial turf.
On natural grass, the ball moves differently, and players need to adjust their speed and touches.
Since Spain’s game relies on sharp passing and rhythm, I believe the grass made it harder for them to play smoothly.

(2) Germany’s Tactical Response – Breaking Down Spain’s Style

In the second half, Germany pressed harder in midfield and closed passing lanes.
With 64 clearances, 44% tackle success, and 13 interceptions, they clearly came prepared to defend as a team.
I think Spain’s lower possession (66%) was the result of Germany’s pressure — and a shock to a team used to total control.

(3) Low Finishing & Fatigue – So Many Shots, Only One Goal

Spain had 22 shots and 9 on target, but just one goal.
Compared to their usual 4 goals per game, this was clearly below their usual standard.
With the match going into extra time, tired legs affected their finishing and focus.
Germany’s physical defending also wore them down.

5. One More Thing – England Is More Dangerous, but Weaker in Defense

Stat	Germany(Avg)	England(Avg)
Goals	1.5	3.25
Expected Goals	2.09	2.92
Big Chances	2.0	5.0
Goals Conceded	1.17	1.83
xG Conceded	1.21	1.52

England plays with a more attacking and aggressive style. They create more big chances and score more goals than Germany.
However, their defense is weaker. They allow more chances and concede more goals.
If England tries to break Spain’s possession like Germany did, they may leave more space behind their defense.
👉 Spain needs to do more than defend. They should try to quickly attack England’s weak defense when they get the chance.

Final Thoughts: Spain’s Control Was Broken — But Talent Won the Day

Spain couldn’t play their usual game — but Athenea and Aitana’s individual brilliance saved them.
The final against England will be even tougher. England is more aggressive and scores more.

So now, the key question is:

"Can Spain get their rhythm back?"

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, matplotlib, Seaborn
Data Source: Flashscore, fbret
Github: https://github.com/k-eunji/ger_es_sf

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

Sure! Here’s the title and table in English as you requested:

[Personal Project #17] UEFA Women’s EURO 2025 Semifinal – The Defending Champion Survives Again

Marina Kim(Eunji) — Wed, 23 Jul 2025 09:59:58 +0000

On 22 July, 2025, the Women’s EURO 2025 semifinal was held.
The defending champion, England, faced Italy, who reached the semifinals with a strong defensive style.
Before the match, an AI prediction (Murning Machine learning) showed England had a 55% chance to win, slightly ahead. As predicted, England won a dramatic 2-1 victory after extra time and advanced to the final.
Here is a data-driven analysis of the match.

Final Score:

England 2-1 Italy (after extra time)

Goal Summary:

33’ First Half Italy Bonansea B.(Cantore S) (0-1)
90+6’ Second Half England Agyemang M. (1-1)
119’ Extra Time England Kelly C. (2-1)

Key Match Data Summary (Based on Flashscore)

Half	Shots (on target)	xG	Big Chances	Possession (%)	Corners	Pass Accuracy (%)
First Half	England 5 (2) Italy 4 (1)	0.49 vs 0.81	0 vs 1	55 vs 45	1 vs 2	86 vs 78
Second Half	England 13 (2) Italy 6 (3)	1.34 vs 0.28	2 vs 1	65 vs 35	6 vs 1	81 vs 71
Extra Time	England 6 (3) Italy 1 (1)	1.09 vs 0.03	2 vs 0	52 vs 48	3 vs 0	83 vs 77
Total	England 24 (7) Italy 11 (5)	2.92 vs 1.12	4 vs 2	58 vs 43	10 vs 3	83 vs 76

1. First Half Analysis (0-1, Italy Leads)

Data: England 5 shots (2 on target), xG 0.49 / Italy 4 shots (1 on target), xG 0.81
Insight:
- England controlled the game with 55% possession and 86% pass accuracy but had no big chances, showing low efficiency.
- Italy scored the first goal at 33 minutes with short counter-attacks and high efficiency.
- The first half was a battle between Italy’s strong defense and England’s weak attack.

2. Second Half Analysis (1-0, England Equalizes)

Data: England 13 shots (2 on target), xG 1.34 / Italy 6 shots (3 on target), xG 0.28
Insight:
- England increased pressure with 65% possession, 6 corners, and successful crosses 10/28.
- Italy’s defense weakened due to fatigue, and possession dropped to 35%.
- The 90+6’ equalizer came from continuous pressure and set-piece use.

3. Extra Time Analysis (1-0, England Wins)

Data: England 6 shots (3 on target), xG 1.09 / Italy 1 shot (1 on target), xG 0.03
Insight:
- Italy’s possession rose to 48% as they tried to stabilize early in extra time.
- England focused on efficiency rather than possession, scoring the winning goal at 119’ from 1 of 2 big chances.
- A defensive error by Italy led to the winning goal.

4. Overall Analysis (Keys to Victory)

1) Strong attack after halftime

Shots in second half + extra time: England 19 vs Italy 7
xG in second half + extra time: 2.43 vs 0.31

2) Italy’s defense weakened by fatigue

Possession dropped to 35% in the second half, reducing their counter-attacks.

3) Effective set-pieces and crosses

Crosses: 13/40 in second half + extra time, corners 9 → good side attacks.

4) Mental strength and late-game focus

Similar to the Sweden match, England showed strong concentration in the second half.
Scored dramatic goals at 90+6’ and 119’, proving the champion’s resilience.

5. Shot xG Scatter Plot by Match Half(Based on Fbref)

The graph below shows shots’ xG (expected goals) and time for first half, second half, and extra time.
It helps compare when and how threatening England’s and Italy’s shots were.

First Half: Italy showed efficient attacks clearly on the xG plot.
Second Half: England increased shots and built up higher xG clearly.
Extra Time: England kept focus to create big chances until the very end.

Final Thought

The defending champion England was blocked by Italy’s strong defense in the first half but changed the game after halftime with overwhelming possession, set-pieces, and crosses, scoring a late equalizer at 90+6’ and a winning goal at 119’ to survive again.

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, matplotlib, Seaborn
Data Source: Flashscore, fbret
Github: https://github.com/k-eunji/eng_it_sf

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

[Personal Project #16] UEFA Women’s EURO 2025 Semifinals: What the Numbers Say About the Final Four

Marina Kim(Eunji) — Mon, 21 Jul 2025 10:24:42 +0000

The semifinals are finally here — 22nd July, 2025
This tournament has been full of surprises: top teams shaking, underdogs fighting back. Now only four teams remain — Spain, England, Germany, and Italy.
Let’s see what the last four matches (group stage + quarterfinal) tell us about their unique styles.

1. Quick Team Averages (4-Match Sample)

Team	Goals	Shots	Shots on Target	xG	Big Chances	Possession %	Pass %	Yellow	Red
Spain	4.0	24.5	8.0	3.21	4.0	74.3	88.8	0.5	0
England	3.25	17.8	5.75	2.92	5.0	61.5	81.5	0.75	0
Germany	1.5	17.0	4.25	2.09	2.75	54.8	75.8	1.75	0.5
Italy	1.25	14.3	5.25	1.61	2.0	41.5	76.8	1.5	0

Quick read:

Spain = heavy possession and pressure.
England = chance-driven attack.
Germany = tough and disruptive.
Italy = low-possession survival mode.

2. Team Stories

1) Spain — “Time on the ball is a weapon.”

24.5 shots, 8 on target, 4 goals per game: huge pressure + great finishing.
74% possession, 89% passing: total control.
Slightly fewer big chances (4.0) than England, but high efficiency.

Key point: Can they control Germany’s strong press? The first 20 minutes will decide the tempo.

2) England — “Big chances break games open.”

Big Chances 5.0 — best of all semifinalists.
3.25 goals, xG 2.92: strong creation and conversion.
61% possession, 82% passing: balance of patience and direct play.

Key point: How fast can they break through Italy’s deep defensive line?

3) Germany — “Tough, stubborn, and disruptive.”

Lower scoring, but defensive activity is top: 61.5% tackles, 22.5 clearances, 10.25 interceptions.
Discipline is risky: 1.75 yellows, 0.5 reds on average.
xG 2.09: they do create, but need better finishing.

Key point: Can they break Spain’s possession without losing control to fouls?

4) Italy — “Low possession, high survival.”

Only 41.5% possession, yet they keep games close.
24.75 clearances, 8 interceptions show their defensive wall.
1.25 goals, xG 1.61: depend on quick counters.

Key point: If England can’t score early, Italy might drag them into a slow game.

3. Let’s Compare with Graphs

When you look at the bar and radar charts, the patterns are crystal clear:

Spain = volume-focused
England = chance-based attack
Italy = defensive survival
Germany = pressing and disruption.

4. Fun Machine-Learning Predictions (Just for Fun!)

I tested a simple model (accuracy only ~57%, so don’t take it too seriously):

England vs Italy

Random Forest: England win probability 0.58
Logistic Regression: England win probability 0.54

Germany vs Spain

Random Forest: Germany win probability 0.27
Logistic Regression: Germany win probability 0.06

Final Thought

The numbers say England vs Italy is a close call, with England’s attacking chances clashing against Italy’s deep defense.
On the other side, Germany vs Spain looks heavily in Spain’s favor. Spain are like a fortress, with control, passing, and relentless pressing.

But can Germany’s physical press break through? If they fail to shake Spain’s build-up in the opening minutes, the match could tilt completely to Spain. Still, if Germany push with intensity and turn the game messy, they might be the only team that can challenge the “red wall.”

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, matplotlib, Numpy, Machine Learning
Data Source: Flashscore
Github: https://github.com/k-eunji/weuro_semi_finals

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

[Personal Project #15] UEFA Women’s Euro 2025: The Defending Champions Almost Fell… But England Survived

Marina Kim(Eunji) — Fri, 18 Jul 2025 11:04:05 +0000

Why Did the Quarter-final vs Sweden End Like This? A Look at the Numbers

On 17th July, 2025, Sweden and England played in the UEFA Women’s Euro 2025 quarter-final. The game ended 2–2 after extra time, and England won 3–2 on penalties. The defending champions were very close to going home, but something saved them.

What made the difference?
Let’s look at the data.

1. Key Match Stats (Quarter-final: Sweden vs England)

Metric	England	Sweden
Goals	2	2
Shots	18	14
Shots on Target	4	7
Expected Goals (xG)	2.50	2.04
Big Chances	3	3
Possession (%)	60	40
Passes (%)	74	59
Fouls	18	15
Tackles (%)	48	53
Clearances	32	50
Interceptions	7	11
Saves	5	2
xGOT Faced	3.74	2.06
Goals Prevented	1.74	0.06

Source: Flashscore

1)Visual: Group Stage vs Quarter-final

Left: England (Group vs Quarter-final)
Right: Sweden (Group vs Quarter-final)

2. Key Points

1) Goalkeeper Saved England

England’s goalkeeper made 5 saves and stopped 1.74 more goals than expected.
Sweden’s goalkeeper made only 2 saves, close to expected.

2) England Controlled the Ball

England had 60% possession and more shots, but fewer shots on target than Sweden.
Sweden was very efficient: more of their shots hit the target.

3) Sweden Defended Hard

Sweden made 50 clearances and 11 interceptions.
England had more tackles and fouls, pressing higher.

3.How Did They Change from the Group Stage?

Metric	England (Group)	England (QF)	Sweden (Group)	Sweden (QF)
Possession (%)	62	60	53	40
Shots	17.7	18	18	14
Shots on Target	6.3	4	5.7	7
Big Chances	5.7	3	4.3	3
Saves	2	5	3	2
Goals Prevented	-0.05	1.74	0.18	0.06

What Does This Show?

England: Goalkeeper performance improved a lot.
Sweden: Lost possession and had fewer chances but stayed dangerous.

4. Goalkeeper Performance (Quarter-final Only)

Metrics: Saves • xGOT Faced • Goals Prevented

5. Why Did England Win?

Factor	Advantage	Why
Goalkeeping	England	+1.74 Goals Prevented; key penalty saves
Possession	England	60% ball control kept Sweden deep
Shot Accuracy	Sweden	More on target, but not enough to win
Defensive Load	Sweden	Heavy defending, but tired late

5. Final Takeaway

England did not win because they scored more in open play—they didn’t. They won because their goalkeeper played much better than expected and stayed strong in the penalty shootout.

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, matplotlib
Data Source: Flashscore
Github: https://github.com/k-eunji/sweden_England_qf

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

[Personal Project #14] Tactical Shift of the 38% Possession Team: How Italy Changed Their Style

Marina Kim(Eunji) — Thu, 17 Jul 2025 14:36:03 +0000

A possession battle similar to Norway’s, but with a different approach.

UEFA Women’s Euro 2025 Quarterfinal — Norway vs Italy

In the UEFA Women’s Euro 2025 quarterfinals, Italy beat Norway 2-1 to reach the semifinals. While neither team was a clear favorite, the way Italy approached the match was very different from their group stage strategy. The data shows how this tactical shift helped them succeed.

The data reveals why this happened.

1. Group Stage Data: Italy Was Clearly a Defensive Team

Here is a summary of the two teams’ group stage averages:

Team	Goals	xG	Big Chances	Possession (%)	Pass Accuracy (%)	Errors → Goal
Italy	1.0	1.46	1.33	38.7	76.3	0.67
Norway	2.67	1.38	2.67	49.3	76.0	0

Insights:

Italy had the lowest possession rate in the tournament → a clear defensive and stable strategy.
Norway showed more goals and big chances, with better attacking efficiency.

2. What Happened in the Quarterfinals?

The quarterfinal match data is as follows:

Team	Goals	xG	Big Chances	Possession (%)	Shots on Target
Italy	2	2.03	4	50	6
Norway	1	2.28	3	50	1

Key changes:

Possession increased to 50% (from 38.7% in the group stage, +11.3%)
Big chances rose to 4 (3 times higher than group average of 1.33)
Risk minimized: Only 2 fouls, 0 errors leading to goals

👉 Italy successfully switched to a more attacking style while keeping defensive stability.

3. Why Was This Change Possible?

Main hypothesis:

Norway focused on midfield possession and defensive stability, but applied low forward pressure, giving Italy more space to build attacks.
Italy shifted from a counter-attack style to gradual build-up play using half-spaces, expanding their attacking options.
The increase in big chances came from stronger wing attacks and more players joining forward positions.

4. Visualization: Before vs After

Below is code to visualize Italy’s improved attacking stats.

5. Conclusion

Based on group stage data alone, Norway seemed slightly favored.
However, style matchup and tactical changes in the knockout phase changed the result.
Italy increased possession and improved build-up play, while maintaining defensive solidity and maximizing attacking efficiency.

👉 What do you think was the biggest reason Italy changed so much?

Tactical adjustments?
Psychological factors?
Norway’s low pressing intensity?

Please share your thoughts in the comments!

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, matplotlib
Data Source: Flashscore
Github: https://github.com/k-eunji/norway_italy_qf

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

[Personal Project #13] Can You Win Without the Ball? Norway vs Italy, a Clash of Counterattack Styles

Marina Kim(Eunji) — Tue, 15 Jul 2025 18:52:18 +0000

UEFA Women’s Euro 2025 Quarterfinal — Norway vs Italy

Kickoff: 16 July 2025, 20:00 (UK Time)

In the Women’s Euro 2025 quarterfinal, Norway and Italy share many similarities. Both Norway and Italy have below 50% possession on average (49.3% and 38.7% respectively) but rely on strong defense and fast counterattacks.
This match features two teams who control less ball but play efficiently.

1. Team Style Comparison

Stats	Norway	Italy
Average Possession (%)	49.3	38.7
Average Shots	10.7	14.7
Average Shots on Target	4.3	5
Average Goals	2.67	1
Average xG	1.38	1.46
Big Chances	2.67	1.33
Tackle Success Rate (%)	61.3	60
Average Clearances	22.3	23.3
Average Saves	4.33	2.33
Allowed HxGOT	2.09	2.79
Goals Prevented (Expected)	+0.09	-0.21

Graph 1: Average Possession Comparison

Norway holds a higher average possession (49.3%) than Italy (38.7%), meaning Norway controls the ball slightly more in matches.

Graph 2: Attack Metrics Comparison

Italy leads in total shots (14.7) and shots on target (5), but Norway outperforms Italy in goals scored (2.67) and big chances created (2.67), showing more clinical finishing and chance conversion.

Graph 3: Defensive Metrics Comparison

Both teams show similar tackle success rates and clearances, but Italy has more errors leading to scoring chances, suggesting a potential weakness in their defensive reliability.

2. Norway: Efficient Use of Space and Quick Timing

Norway has moderate possession but creates many chances. They score 2.67 goals per game with an xG of 1.38 and make 2.67 big chances. Their goalkeeper is key, making 4.33 saves per game and stopping more goals than expected (+0.09).

Key Points:

Efficient use of space by penetrating attackers
Rapid chance creation by exploiting opponent defensive transitions
Stable defensive line with effective finishing

3. Italy: Structured Defense and One-Touch Attacks

Italy has low possession (38.7%) but creates many shots (14.7) and shots on target (5). However, they score fewer goals (1.0) and have fewer big chances (1.33) than Norway. Their defense allows a high expected goals on target (2.79), and their negative goals prevented (-0.21) suggests some defensive or goalkeeping weaknesses.

Key Points:

Traditional deep defensive line setup
Direct attacks relying on limited but quality chances
Some doubts remain about defensive consistency

4. Defensive Statistics Breakdown

Stats	Norway	Italy
Tackle Success Rate	61.3%	60%
Clearances	22.3	23.3
Interceptions	5	8.7
Errors Leading to Shots	0	0.67

Italy leads in interceptions, showing good defensive organization. However, their 0.67 errors leading to scoring chances could be risky in a high-stakes quarterfinal. Norway’s zero errors here suggest greater defensive stability.

5. Conclusion: Who Has the Edge?

Norway is favored to win.

Norway shows more consistent performance, better big chance creation, and stronger goal prevention.
Italy has higher shot volume but less efficiency in threat and finishing.
Defensive mistakes pose a bigger risk for Italy.

👉 Considering balance and transition efficiency, Norway seems one step ahead.

6. Final Takeaway

Possession isn’t key. When both teams have less ball, the one who uses chances better wins. Let’s see who proves it tomorrow.

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas
Data Source: Flashscore
Github: https://github.com/k-eunji/norway_italy

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

[Personal Project #12] Tactical Insights from UEFA Women’s EURO 2025: 8 Quarterfinal Teams Analyzed

Marina Kim(Eunji) — Mon, 14 Jul 2025 15:10:23 +0000

The UEFA Women’s EURO 2025 group stage ended on July 13. The knockout stage will begin with Norway vs Italy on July 16. Based on match data from the group stage, I analyzed the tactics and styles of the eight quarterfinal teams.

Which teams controlled games with possession? Which teams focused on solid defending and effective counterattacks? Here’s what the numbers show.

1. What Top Teams Have in Common

1). Possession and Pass Accuracy – “Control the ball, control the game”

The top teams had something in common:
High possession rates and excellent pass success.

Team	Possession (%)	Pass Accuracy (%)
Spain	75.0	89.0
England	62.0	84.0
Germany	64.7	83.7

🔸These teams used solid build-up play and quick ball circulation to dominate matches and create many chances.

2) Clean Defense and Smart Pressure

Besides attack, strong and organized defending was also key.

Most of the quarterfinal teams allowed fewer than 0.3 shots due to errors per game.
They also used high pressing to defend in the opponent’s half.

Team	Clearances	Interceptions	Goals from Mistakes
Spain	11.0	8.0	0.3
England	18.7	6.0	0.3
Germany	14.0	9.0	0

🔸Germany had higher defensive actions (clearances, interceptions), but overall, these teams controlled both offense and defense through smart possession and pressure.

2. Two Opposites in Style – Spain vs Norway

1) Spain: The Modern ‘Tiki-Taka’ Machine

Possession: 75%
Average shots: 25 per game
xG (expected goals): 3.05
Goals per game: 4.67
Pass accuracy: 89%

🔸Spain dominated every match with their attacking style.
They had the highest number of shots and chances among all 8 teams.
Spain’s football was all about controlling, passing, and scoring.

2) Norway: Efficient and Organized Defense

Possession: 49.3%
Goals per game: 2.67
xG: 1.38
Tackle success: 61.3%
Saves per game: 4.33
Mistakes leading to goals: 0

🔸Norway had the lowest possession among the top 8,
but showed strong defending and excellent finishing.
They allowed few mistakes and took their chances well.

Stat	Spain	Norway
Possession (%)	75.0	49.3
Goals per game	4.67	2.67
Shots per game	25.0	10.67
Shots on target	8.67	4.33
xG (Expected goals)	3.05	1.38
Tackle success (%)	59.3	61.3
Goals from mistakes	0.33	0.0
Saves per game	1.00	4.33

3. Tactical Summary – Based on Data

1) Possession-Based Style: Spain, Germany, England

→ High possession, short passes, pressing from the front.

Team	Possession (%)	Pass Accuracy (%)	Clearances	Interceptions	Goals from Mistakes
Spain	75.0	89.0	11.0	8.0	0.3
Germany	64.7	83.7	14.0	9.0	0
England	62.0	84.0	18.7	6.0	0.3

→ These teams built attacks patiently, pressed high, and made few mistakes in defense.

2) Defensive & Efficient Style: Norway, Italy

→ Low possession, strong defense, fast counters or set-pieces.

Team	Possession (%)	Goals	Clearances	Tackle Success (%)	Saves	Goals from Mistakes
Norway	49.3	2.67	22.3	61.3	4.33	0.0
Italy	38.7	1.00	23.3	60.0	2.33	0.7

→ These teams defended deep and focused on counterattacks or set-pieces.
Despite lower possession, they scored enough goals to advance.

3) Balanced Style: France, Switzerland, Sweden

→ Mix of possession and defense, good structure and low errors.

Team	Possession (%)	Pass Accuracy (%)	Tackles	Interceptions	Goals from Mistakes
France	55.3	80.6	67.0	7.3	0.3
Switzerland	54.3	78.3	58.0	8.0	0.3
Sweden	53.3	77.3	59.0	7.0	0

→ These teams were well-balanced, had good passing ability, and showed reliable team defense.

✅ Tactical Summary Table

Type	Teams	Key Features
Possession	Spain, Germany, England	Build-up play, control with passes, high pressing
Efficient	Norway, Italy	Strong defense, counters or set pieces
Balanced	France, Switzerland, Sweden	Good structure, solid balance in attack and defense

4. Final Takeaway

The group stage of WEURO2025 showed one clear message:

"Possession is power, but mistakes are deadly."

Still, teams like Norway proved that efficiency and defense can beat possession.

The quarterfinals are single-match knockouts.
Will Spain continue to dominate? Or will a team like Norway cause another surprise?

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas
Data Source: Flashscore
Github: https://github.com/k-eunji/-group_insights_weuro2025

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!

[Personal Project #11] UEFA Women’s Euro 2025 - Group B: Who Gets 2nd Place?

Marina Kim(Eunji) — Fri, 11 Jul 2025 08:25:02 +0000

The group stage is almost done. Spain already qualified for the quarter-finals, but second place is still open.

1. Group B Standings (after 2 matches)

Team	Pts	GD	GS	GA
Spain	6	+9	11	2
Italy	4	+1	2	1
Portugal	1	-5	1	6
Belgium	0	-5	2	7

✅ Spain is already in the quarter-finals.
Italy is in 2nd place now.
Portugal still have a chance.

2. Last Group Matches

Spain vs Italy
Portugal vs Belgium

1) Portugal vs Belgium – Who Is Better?

Average Stats (2 matches):

Matrics	Portugal	Belgium
Goals per match	0.5	1.0
Expected Goals (xG)	0.79	0.74
Shots on target	1.0	2.5
Possession (%)	50.0	36.5
Goals conceded	3.0	3.5

_Insight>
Portugal dominates possession and passes better, while Belgium has slightly more output up front.
Portugal has the edge, especially if they tighten up defensively.

2) Spain vs Italy – Can Italy Stop Spain?

Average Stats:

Metric	Spain	Italy
Goals per match	5.5	1.0
Expected Goals (xG)	3.60	0.86
Possession (%)	75.5	50.0
Big chances	10	1

Insight>
According to the data, Spain has shown overwhelming performance so far. Italy’s defense is solid, but their attack is very weak (only 1 goal in 2 games). Italy only needs a draw to reach the quarter-finals, but it won’t be easy. Spain is expected to win by at least 2 goals.

3. Qualification Scenarios

Group B Qualification Scenarios – Who Gets 2nd Place?

Scenario	Italy Points	Portugal Points	2nd Place Outcome
Spain beats Italy, Portugal wins big	4	4	Depends on goal difference
Spain wins, Portugal wins narrowly	4	4	Italy likely (better GD)
Italy draws vs Spain	5	≤4	Italy qualifies
Italy beats Spain	7	≤4	Italy wins the group
Italy and Portugal tied on points and GD, their match was a draw	4	4	Portugal likely advances by total goals scored

Explanation

If Italy and Portugal have the same points and goal difference (GD), the result of their head-to-head match is checked.
If their head-to-head match was a draw, then the team with more total goals scored in the group advances.
So, if Italy loses by 2 goals to Spain, and Portugal wins by 3 goals, both end up with 4 points and the same GD (-1).
Since their match was a draw, Portugal would likely advance because they have scored more goals overall.
Italy only needs a draw against Spain to secure 2nd place without depending on other results.

4. Final Prediction (based on data)

✨ Italy is the most likely team to advance as 2nd place, unless Portugal pulls off a big win and Italy loses badly.

🙋‍♀️About Me

Hi, I’m Marina Kim (Eunji), a sports data content creator learning and sharing football stories through data.

Tools: Python, pandas, seaborn
Data Source: Flashscore
Github: https://github.com/k-eunji/group_b_weuro2025

I’m open to new opportunities in sports data and content creation. Let’s connect if you’re interested!