Tinder Statistics 2020-2025: The Complete Data Analysis

About This Data

This analysis is based on 7,079 Tinder profiles uploaded to SwipeStats.io between 2020-2025. Users downloaded their data directly from Tinder and uploaded it for analysis, providing us with unprecedented insight into real dating app behavior.

What's included:

• Complete swipe history (likes sent, matches received)
• Message data (conversations, response rates, ghosting)
• Profile information (bio, job, education, photos)
• App usage patterns (daily activity, limit hits)
• Temporal data (when swipes and matches occurred)

Total data analyzed:

• 294 million right swipes
• 3.14 million matches
• 8.7 million messages
• 7,079 unique profiles

Data Disclaimer: Users self-select their gender when uploading to SwipeStats. While most select correctly, some discrepancies may exist. Additionally, message data only shows messages sent by the user. See Methodology for full details on data limitations.

We shared this dataset with Zackary Smigel for an in-depth video analysis that reached over a million views. Watch it here or keep reading for the full breakdown:

Sample Size & Demographics

Overall Dataset Composition

Metric	Male	Female	Total
Total Profiles	6,235 (88.1%)	844 (11.9%)	7,079
Profiles with Complete Stats	6,233 (99.97%)	842 (99.76%)	7,075
Total Right Swipes	261.7 million	32.3 million	294 million
Total Matches	2.56 million	582,030	3.14 million
Total Messages Sent	7.4 million	1.36 million	8.76 million

The 88% male / 12% female split closely mirrors Tinder's reported user base. Men account for 89% of all right swipes but only 81% of all matches.

Average Age by Gender

Gender	Average Age	Median Age
Male	27.8 years	—
Female	26.7 years	—
Overall	27.6 years	—

Match Rates: The Core Metric

Match rate is the single most important metric in dating app analysis. It's calculated as:

Match Rate = (Matches / Right Swipes) x 100

Overall Match Rate Statistics

Metric	Male	Female	Ratio
Average Match Rate	5.26%	44.39%	1:8.4
Median Match Rate	2.04%	41.27%	1:20.2
Sample Size	6,233	842	—

Why the median matters: The average is skewed by top performers. The median tells you what a "typical" user experiences. For men, the reality is even harsher than the average suggests. Half of all men get a match rate below 2.04%.

Match Rate Distribution (Percentiles)

Percentile	Male Match Rate	Female Match Rate	What This Means
Top 1%	45.15%	160.23%	Elite performers (Super Likes, resets)
Top 5%	20.37%	79.43%	Exceptional results
Top 10%	12.50%	70.00%	Very successful
Top 25%	5.39%	55.59%	Above average
Top 50% (Median)	2.04%	41.27%	Typical user
Bottom 25%	0.76%	27.21%	Below average
Bottom 10%	0.30%	15.87%	Struggling
Bottom 5%	0.14%	11.39%	Severe difficulty

What These Numbers Mean in Practice

For men:

• If your match rate is 5%, you're in the top 25% of male users
• If your match rate is 2%, you're at the median
• If your match rate is 12%+, you're in the top 10%
• A 2% match rate means 1 match for every 50 right swipes
• A 0.3% match rate (bottom 10%) means 1 match for every 333 right swipes

For women:

• The median woman gets 41% match rate. Nearly matching with half of people she likes.
• Even the bottom 10% of women (15.87%) outperform the top 10% of men (12.5%)
• The gender gap is the defining feature of dating app statistics

Activity & Engagement Metrics

Swipe Volume & Matches

Metric	Male	Female	Difference
Average Likes Sent (Total)	15,609	2,109	Men swipe 7.4x more
Average Matches (Total)	410	691	Women get 69% more
Average Days Active	36.34	8.55	Men active 4.3x longer
Sample Size	6,233	842	—

Men compensate for lower match rates with massive volume. Despite swiping 7.4x more, women still accumulate 69% more total matches. Men stay on the app 4.3x longer. The efficiency gap is enormous.

Messaging Behavior

Metric	Male	Female	Analysis
Avg Messages Per Match	4.85	3.81	Men send ~1 more message per match
Avg Messages Received Per Match	4.32	4.44	Similar incoming when convos happen
Response Rate	84.84%	140.36%	140%+ means women send multiple messages
Avg Conversation Length	7.62 msgs	7.79 msgs	Similar depth when connected
Max Conversation Length	98.7 msgs	130.9 msgs	Women's longest convos 33% longer

Message Type Breakdown

Message Type	Male %	Male Count	Female %	Female Count
Text	99.11%	7,331,140	99.44%	1,355,309
GIF	0.53%	39,152	0.39%	5,315
Contact Card	0.20%	14,441	0.13%	1,752
Activity	0.06%	4,167	0.02%	226

GIFs are surprisingly underused (<1%). This could be an opportunity to stand out.

Super Likes: Do They Work?

Metric	Male	Female
Average Super Likes Sent	93.7	4.8
Median Super Likes Sent	1	0
Top 1% Super Likes Sent	3,248	—
Top 10% Super Likes Sent	705	—

Most users don't bother with Super Likes at all (median: 1 for men, 0 for women). But a small group of men go all in.

Here's the problem: it doesn't work.

User Group	Super Likes Sent	Match Rate
Top 1% Super Like users	3,248	1.94%
Top 10% Super Like users	705	2.29%
Average male user	93.7	5.26%

Men who send the most Super Likes get worse match rates than the average male user. The top 1% of Super Like users (3,248 sent) had a 1.94% match rate — below the 2.04% median. Spending money on Super Likes may actively signal desperation rather than interest.

Selectivity: Quality vs Quantity

One of the most important findings in our data: being selective dramatically improves your match rate.

Swipe Right Percentage Distribution

Selectivity Level	Male Users	Male %	Female Users	Female %
<30% (Very Selective)	2,920	46.9%	800	95.2%
30-50% (Moderate)	1,522	24.4%	24	2.9%
50-70% (Liberal)	1,028	16.5%	6	0.7%
70-90% (Very Liberal)	537	8.6%	3	0.4%
90%+ (Swipe on Everyone)	203	3.3%	0	0%

95% of women swipe right on less than 30% of profiles. Only 47% of men are this selective.

Selectivity and Match Rate

Swipe Right %	Male Match Rate	Female Match Rate	Male Users	Analysis
<30% (Selective)	6.84%	45.01%	2,920	Best results
30-50%	4.00%	30.92%	1,522	-41% from selective
50-70%	3.57%	24.17%	1,028	Continued decline
70-90%	4.49%	23.64%	537	Slight uptick
90%+	2.19%	—	203	Worst results

Male Selectivity Deciles: The Full Picture

Decile	Avg Swipe Right %	Match Rate	Users
1 (Most Selective)	3.79%	11.85%	621
2	9.50%	7.35%	621
3	15.49%	5.60%	621
4	21.83%	4.34%	621
5	28.54%	4.31%	621
6	35.85%	4.18%	621
7	43.76%	3.64%	621
8	53.50%	3.76%	621
9	65.67%	3.48%	621
10 (Least Selective)	85.67%	3.95%	621

The most selective men (swiping right on <4% of profiles) get 11.85% match rate. More than DOUBLE the average.

Female Selectivity Deciles

Decile	Avg Swipe Right %	Match Rate	Users
1 (Most Selective)	0.58%	62.01%	84
2	1.41%	52.80%	84
3	2.15%	46.41%	84
4	3.10%	44.64%	83
5	4.29%	42.68%	83
6	5.73%	42.69%	83
7	7.80%	42.24%	83
8	10.44%	40.59%	83
9	14.87%	35.52%	83
10 (Least Selective)	31.92%	33.84%	83

The most selective women swipe right on less than 1% of profiles. Even the "least selective" women (31.92%) are more selective than 53% of men.

Being selective (<30% swipe rate) vs. swiping on everyone (90%+):

• Match rate difference: 6.84% vs 2.19% = 3.1x higher
• Practical impact: 1 match per 15 swipes vs 1 match per 46 swipes

Want to know how your selectivity compares? Upload your Tinder data and see your real numbers.

Bio Analysis: What to Write

Bio Length and Match Rate

Bio Length	Male Match Rate	Male Users	Male %	Female Match Rate	Female Users
Empty (0 chars)	5.34%	1,539	24.7%	42.95%	276
Short (1-50 chars)	6.87%	1,363	21.9%	46.28%	168
Medium (51-150 chars)	5.22%	1,569	25.2%	44.41%	199
Long (151-300 chars)	3.97%	1,037	16.6%	43.95%	105
Very Long (300+ chars)	3.96%	713	11.4%	45.77%	91

For men, short bios dramatically outperform:

• Short (1-50 chars): 6.87% match rate
• Long (151-300 chars): 3.97% match rate
• 73% better with a short bio

For women, bio length barely matters. All lengths cluster between 42.95% - 46.28%.

Bio Feature Impact on Match Rates

Feature	Male With	Male Without	Impact
Emojis	5.20%	5.29%	-2%
Humor mention	5.30%	5.23%	+1%

Emojis have virtually no impact on match rates. Explicitly mentioning humor actually hurts women's match rates by 15%. Saying you're funny is not the same as being funny.

Profile Features That Impact Matches

Job Title: The Counterintuitive Finding

Feature	Male Match Rate	Male Users	Male %
No Job Title	6.29%	2,559	41.1%
Has Job Title	4.54%	3,662	58.9%

Men without a job title listed get 39% more matches than those who display their occupation.

Education Display

Feature	Male Match Rate	Male Users
No Education Listed	5.36%	5,782
Has Education Info	3.90%	437

Not listing education correlates with 37% higher match rates for men.

Summary: Profile Feature Impact

Feature	Male Impact	Recommendation
Job Title	-39% if shown	Don't list it
Education	-37% if shown	Don't list it
School	+3% if shown	Optional
Instagram	-2% if shown	Doesn't matter
Emojis	-2% if used	Doesn't matter

The pattern: For men, less information = more matches. Mystery and curiosity outperform resume-building on dating apps.

Age & Match Rates

Age and Match Rate (The Surprising Finding)

Age Group	Male Match Rate	Female Match Rate	Male Trend	Female Trend
18-21	7.54%	43.03%	Peak	Average
22-25	5.49%	42.66%	Declining	Average
26-29	5.29%	45.98%	Average	Above avg
30-34	4.17%	42.70%	Below avg	Average
35-39	3.78%	51.42%	Low	Rising
40-44	3.87%	55.36%	Low	Peak
45+	4.68%	38.31%	Slight uptick	Declining

For men: Match rates peak at 18-21 (7.54%) and decline through the 30s. 18-21 year olds get 2x the match rate of 35-39 year olds.

For women: Match rates INCREASE with age until 40-44. Women aged 40-44 get 29% more matches than 18-21 year olds. Fewer women remain on dating apps as they age, making those who stay more valuable due to scarcity.

Usage Patterns & App Behavior

Usage Intensity and Match Rate

Usage Level	Male Match Rate	Male Users	Male %
Light (<100 swipes)	3.55%	126	2.0%
Moderate (100-500)	4.01%	891	14.3%
Regular (500-1K)	4.64%	777	12.5%
Heavy (1K-5K)	5.74%	2,614	42.0%
Extreme (5K+)	5.57%	1,810	29.1%

Daily Swipe Limit: The Striking Finding

Limit Hit Frequency	Male Match Rate	Male Users	Male %
Never (0 times)	20.87%	81	1.3%
1-5 times	5.99%	732	11.8%
6-20 times	5.69%	1,371	22.0%
21-50 times	4.93%	1,287	20.7%
50+ times	4.54%	2,750	44.2%

Men who never hit the daily swipe limit achieve a 20.87% match rate. Nearly 4x the average. Small sample (81 users), but the pattern is clear: restraint correlates strongly with success.

Conversation Analysis

Conversation Depth Distribution

Conversation Depth	Male %	Male Matches	Female %	Female Matches
No Messages (0)	10.96%	114,811	30.80%	68,721
1 Message Only	32.44%	339,823	22.41%	50,006
Short (2-5 msgs)	30.73%	321,917	23.79%	53,082
Medium (6-10 msgs)	10.93%	114,446	9.51%	21,222
Good (11-20 msgs)	7.72%	80,843	6.94%	15,488
Long (21-50 msgs)	5.14%	53,794	4.71%	10,505
Very Long (50+ msgs)	2.09%	21,850	1.84%	4,110

For men: 43.4% of matches result in 0-1 messages. Only 14.95% become real conversations (11+ messages). Only 2.09% reach deep connection territory (50+ messages).

Ghosting & One-Message Statistics

Metric	Male	Female
% One-Message Conversations	32.83%	21.19%
Conversations Ending After User's Message	4.25%	11.53%
Avg Count of Such Conversations	18.4	81.6

Women's conversations end after their message at a 2.7x higher rate (11.53% vs 4.25%).

Data caveat: We only see messages sent by the user. "Ghosting rate" here means "conversations ending after user's last sent message." Some of these may be cases where the user chose not to continue, not where they were ghosted.

Temporal Patterns: When to Swipe

Day of Week Performance

Day	Male Match Rate	Female Match Rate
Sunday	2.54%	30.63%
Monday	2.50%	30.35%
Tuesday	2.48%	30.15%
Wednesday	2.49%	30.45%
Thursday	2.51%	30.90%
Friday	2.44%	29.91%
Saturday	2.47%	30.10%

Best days: Sunday for men (2.54%), Thursday for women (30.90%). Worst day: Friday for both genders.

Monthly Performance

Month	Male Match Rate	Female Match Rate
January	2.41%	30.50%
February	2.36%	31.99%
March	2.32%	29.73%
April	2.59%	31.59%
May	2.42%	32.61%
June	2.52%	32.20%
July	2.58%	28.64%
August	2.55%	30.61%
September	2.61%	29.18%
October	2.59%	28.56%
November	2.50%	30.29%
December	2.41%	27.77%

Best months: September for men (2.61%), May for women (32.61%).

Cuffing Season: Myth or Reality?

Season	Male Match Rate	Female Match Rate
Cuffing (Oct-Mar)	2.46%	29.81%
Rest of Year (Apr-Sep)	2.51%	30.70%
Difference	-2%	-3%

The data says cuffing season is a myth. Match rates are actually slightly LOWER during "cuffing season." Fewer users are active during colder months.

Geographic Analysis: Best Cities

Top Cities for Male Match Rates

Rank	City	Match Rate	Users
1	San Francisco	15.22%	14
2	Rochester	13.58%	8
3	Melbourne	12.32%	14
4	Austin	11.53%	12
5	Warsaw	10.77%	16
6	Glasgow	10.72%	9
7	Zagreb	10.66%	9
8	Buenos Aires	10.11%	12
9	Edinburgh	9.70%	7
10	Bangkok	9.56%	7

Major Cities Performance

City	Match Rate	Users	vs Average
San Francisco	15.22%	14	+189%
New York	6.82%	45	+30%
Chicago	6.78%	24	+29%
Boston	6.86%	19	+30%
Los Angeles	6.94%	14	+32%
Paris	5.24%	30	0%
Berlin	4.78%	38	-9%
London	4.30%	30	-18%
Stockholm	3.53%	30	-33%
Budapest	2.88%	41	-45%

Match rates range from 15.22% (San Francisco) to 2.88% (Budapest). A 5x difference based on location alone.

Note: Most city samples are small (<50 users). These results are directional but not statistically robust.

Job Titles & Match Rates

Top Performing Job Titles (Male)

Rank	Job Title	Match Rate	Users	vs Average
1	Investment Analyst	23.27%	3	+342%
2	Bartender	13.87%	8	+164%
3	Law Student	13.11%	4	+149%
4	Barista	11.23%	6	+113%
5	Tech	10.90%	6	+107%
6	Marketing	9.33%	6	+77%
7	Analyst	9.22%	10	+75%
8	Entrepreneur	8.16%	12	+55%
9	Consultant	7.79%	20	+48%

Worst Performing Job Titles (Male)

Rank	Job Title	Match Rate	Users	vs Average
1	DevOps Engineer	1.05%	6	-80%
2	Electrician	0.76%	3	-86%
3	IT Consultant	0.93%	7	-82%
4	Data Engineer	1.41%	6	-73%
5	Accountant	1.44%	7	-73%
6	Designer	1.47%	8	-72%
7	Research Assistant	1.79%	11	-66%
8	Sales	1.87%	8	-64%
9	Developer	2.00%	18	-62%
10	Lawyer	2.02%	5	-62%

The Tech Job Penalty

Tech Job	Match Rate	Users	vs Average
"Tech" (generic)	10.90%	6	+107%
Software Engineer	3.95%	208	-25%
Data Scientist	2.23%	23	-58%
DevOps Engineer	1.05%	6	-80%
Software Developer	3.81%	85	-28%
Data Engineer	1.41%	6	-73%

The more specific your tech title, the worse your match rate. Generic "Tech": 10.90%. "Software Engineer": 3.95%. "DevOps Engineer": 1.05%.

Recommendation: If you work in tech, consider listing nothing or a generic term like "Tech" rather than your specific title.

Year-Over-Year Trends

2024 vs 2025 Comparison

Metric	Male 2024	Male 2025	Change	Female 2024	Female 2025	Change
Sample Size	3,072	3,163	+3%	500	344	-31%
Avg Match Rate	4.74%	5.76%	+21%	41.56%	48.54%	+17%
Median Match Rate	2.36%	2.04%	-28%	40.67%	41.27%	+4%
Average Age	29.2	26.5	-9%	27.3	25.9	-5%
Avg Days Active	38.67	11.44	-70%	44.0	17.73	-60%
% One-Msg Convos	33.44%	32.23%	-4%	21.43%	20.83%	-3%
Ghosting Rate	4.62%	3.89%	-16%	11.81%	11.12%	-6%

The Inequality Paradox

The most important trend: average up but median down for men.

This means: Top performers are doing even better than before. Average and below-average users are doing worse. The gap between winners and losers is widening. Dating app success is becoming more concentrated among fewer users.

Sexual Orientation Breakdown

Gender	Interested In	Count	% of Gender
Male	Female	5,944	95.33%
Male	Male	256	4.11%
Male	Unknown	35	0.56%
Female	Male	606	71.80%
Female	Female	114	13.51%
Female	Unknown	124	14.69%

Women show higher rates of same-sex interest (13.5% vs 4.1%). Match rate analyses in this article primarily reflect heterosexual dynamics due to dataset composition.

Top vs Bottom Performers

What Separates Winners from Losers?

Metric	Top 10% Males	Bottom 10% Males	Difference
Match Rate	12.5%+	<0.30%	42x
Avg Days Active	90.7	132.8	-32%
Avg Bio Length	75.76	81.99	-8%
Selectivity (Swipe %)	48.64%	67.85%	-28%

Top 10% male users are more selective (49% vs 68%), spend less time on the app (91 days vs 133 days), and have slightly shorter bios (76 chars vs 82 chars). They achieve better results with less effort. Bottom performers put in more effort for worse results.

Methodology

Data Collection

• Source: SwipeStats.io user uploads
• Period: 2020-2025 (primarily 2024-2025)
• Method: Users download their data directly from Tinder and upload to SwipeStats
• Total profiles: 7,079

Strengths

• Real user data directly from Tinder's official data export
• Large sample size (7,000+ profiles)
• Comprehensive metrics (swipes, matches, messages, profile data)
• Longitudinal data (multi-year)

Limitations

• Selection bias: Users who track their stats may differ from average users
• Self-reported gender: Users must confirm their gender via SwipeStats before uploading. While most users select correctly, some may choose incorrectly, which could slightly skew gender-specific statistics
• Geographic bias: Primarily English-speaking and European users
• Small samples: Many subgroup analyses (cities, jobs) have <50 users
• Age filter anomalies: Some age filter data contains impossible values and should be interpreted directionally only

Important Caveats About Message Data

What we can see:

• Total messages sent by the user
• Total messages received by the user (aggregate count only)
• Conversation threads and their lengths

What we cannot see:

• Individual received message content or timing
• Whether a user received a reply but chose not to respond
• Read receipts or message delivery status

The ghosting rate is calculated based on conversations that ended after the user sent a message without receiving a reply. "Ghosting rate" should be interpreted as "rate of conversations ending after user's last sent message" rather than a true measure of being ghosted.

Definitions

• Match Rate: Matches / Right Swipes x 100
• Ghosting Rate: % of conversations ending after user's last sent message
• One-Message Conversation: Matches with exactly one message in the thread
• Days Active: Days with at least one swipe recorded
• Cuffing Season: October through March
• Selectivity Decile: Users divided into 10 equal groups by swipe right percentage

The Bigger Picture: Dating Apps and the Loneliness Epidemic

These numbers don't exist in a vacuum. The WHO has declared loneliness a global health concern, with the U.S. Surgeon General comparing its health impact to smoking 15 cigarettes daily. Meanwhile, roughly 60% of relationships now start online, making dating apps the default way most couples meet.

That creates a paradox: the tools designed to connect us may be contributing to our isolation.

Our data helps explain why. The year-over-year trends show average match rates going up while median match rates go down. The gap between winners and losers is widening. Paid features like Super Likes don't improve outcomes — they may actually hurt them. 43% of matches never become real conversations.

As Zackary Smigel put it in his analysis of our data: "The worse the experience gets, the more we buy into the idea that we are the problem." Dating apps have a business model that monetizes the search for connection rather than facilitating it. The frustration isn't a bug — it's what keeps users paying.

Understanding this doesn't fix it. But it does mean you can stop blaming yourself when the math is stacked against you from the start.

Conclusion

The Fundamental Asymmetry

Women hold significant structural advantages on dating apps, with 8.4x higher match rates and far less effort required.

What Actually Works

Counterintuitively, less is more on dating apps:

• Shorter bios outperform longer ones
• Not listing your job beats listing it
• Being selective triples match rates vs. swiping on everyone
• Restraint (not hitting limits) correlates with 4x better results
• Super Likes don't work — heavy users get worse results than average

Practical Recommendations

For men:

1. Be highly selective (<30% swipe rate)
2. Keep your bio short (1-50 characters)
3. Don't list your job title or education
4. Swipe on Sundays and in September
5. Focus on quality over quantity
6. Don't waste money on Super Likes

For women:

1. Bio optimization matters less, but moderate app usage is optimal
2. Thursday is your best day
3. May is your best month
4. Age works in your favor. Match rates increase into your 40s.

For everyone:

1. Most matches go nowhere. Only 15% become real conversations.
2. Location matters enormously. Consider where you're swiping.
3. Restraint and selectivity outperform desperation and volume.
4. The system is designed to keep you frustrated. Know the math before you blame yourself.

Want to see where you actually stand? Upload your Tinder data and get your real numbers.