Is that new chart arrival really a new competitor?

Most “new” chart arrivals were returning apps.

About three in four Apple arrivals and two in three Google Play arrivals had already appeared in the same country’s top 100 during the previous two weeks.

Prepared 27 October 2026 · Arrival dates: 3 to 26 October 2026, UTC · Earlier observations provide history · 1,465 arrivals with complete 14-day history · 811 store-specific app IDs

An app entering the top 100 was often a familiar app coming back after a short absence. The majority-returning pattern appeared in both stores and both time periods, and survived checks using different dates, timing rules and treatment of repeat arrivals.

1. What we found

We identified apps present in today’s complete top 100 chart but absent from the previous daily chart. Then we checked the preceding 14 daily charts for the same app ID in the same store and country.

529 of 694 Apple arrivals were returns. On Google Play, 509 of 771 were returns. These were observed prior appearances, not guesses based on an app’s name or reputation.

“Not seen in the prior 14 days” does not mean newly launched. An app could have existed for years or appeared in the chart before our lookback period. We did not have the release evidence needed to classify launches.

The headline counts arrival events. The same app can arrive more than once or appear in more than one country. The primary sample covers 190 chart-date comparisons across seven countries in each store. The stores have different app populations.

2. Amazon Prime Video generated five arrivals in Germany

In Apple’s German chart, Amazon Prime Video entered the top 100 on five dates in the study sample. Every one was a confirmed return.

That is five arrival events involving one app in one country. On 14 October it returned at #44, so returns were not confined to apps just scraping into #100.

One Google Play app generated seven arrivals in India

FRND: Talk to Friends Online appeared as an arrival on 5, 8, 14, 16, 19, 22 and 24 October. It had been observed in the Indian chart before every one of those arrivals.

Its absences lasted one to four daily observations. The repeated events describe one app moving in and out of the recorded top 100, rather than seven different competitors.

These examples were selected after analysis to illustrate repeated arrivals. Full timestamps, stable app IDs, prior appearance records and source snapshot IDs are included in the evidence. We also checked that each example was absent from its preceding complete chart.

3. Counting arrivals overstates the number of distinct competitors

The return rate and the number of repeated arrival events answer different questions. An app can return from an appearance before our study period yet generate only one arrival inside it. We therefore counted distinct app-country combinations separately.

Apple’s 694 arrivals involved 448 distinct app-country combinations. Google Play’s 771 arrivals involved 506. Counting each combination once reduced the totals by about 35% in each store.

An app in Germany and the same app in the US remain two separate app-country combinations here. This reduction comes from repeated arrivals within a country, not from merging countries together.

Two numbers that should not be confused:76.2% of Apple arrivals were confirmed returns within 14 days. Separately, 35.4% of its recorded arrival events were additional events beyond one per app-country combination during the study sample.

4. Many apps returned after a short absence near the chart boundary

Returning Apple apps were typically missing from three daily charts; Google Play apps from two. Most returns landed in positions 81 to 100.

That helps explain why an arrival list can look busy even when the competitors are familiar. Apps near the cutoff can disappear from a top 100 view and come back shortly afterwards.

But the finding was not limited to the last 20 positions. Among arrivals at #80 or better, the full-sample returning shares were 71.86% on Apple and 59.91% on Google Play.

“Absent” means missing from the selected daily top 100 snapshots. It does not mean removed from the app store or continuously absent between observations. We cannot see intraday departures or returns.

5. The result repeated, including in later dates

The later-period figures were 75.96% for Apple and 66.05% for Google Play. Both uncertainty methods remained above 50% in both stores.

App resampling keeps an app’s countries and dates together. Date-block resampling keeps groups of neighbouring dates together. These address different forms of repeated observations; they do not validate the collector or prove the same rates hold across the whole app market.

Repeatedly returning apps did not explain the entire majority

Keeping only the first eligible arrival for each app-country combination in the later period lowered the shares to 68.35% and 55.82%. Frequent comebacks strengthened the headline result, but the majority remained in this check. These sensitivity percentages are descriptive; we did not calculate a separate confidence interval for every check.

Looking back further found additional returns

We compared 14-day and 21-day history on exactly the same arrivals with complete 21-day coverage. Keeping the event sample fixed avoids confusing a longer history with different countries or dates.

Some apps classified as not seen in the previous 14 days became confirmed returns with another week of history. Neither classification establishes the age or launch date of an app.

6. Missing history mattered, so we checked it explicitly

The strict 14-day requirement excluded many arrivals. Missing charts cannot be treated as evidence that an app was absent.

The primary 1,465-arrival sample came from 190 complete-history chart windows. Another 1,393 arrivals had incomplete history and were excluded from that main calculation.

For 940 of those excluded arrivals, an earlier appearance was still directly observed. Missing other dates did not erase that evidence of a return. Another 453 could not be classified reliably.

Even if all 453 unresolved cases were treated as non-returns, confirmed returns still made up the following shares of all 2,858 arrivals with valid daily endpoints:

This broader lower-bound check supports the majority finding without assuming what happened on missing dates. It is separate from the complete-history sample used to measure absence length.

The country coverage is narrower than the headline dataset

Complete 14-day UTC history was available in Brazil, Germany, the UK, India, Japan, South Korea and the US. In the later primary sample, Apple coverage narrowed to Germany, India and the US; Google Play covered Brazil, Germany, the UK, India and the US.

We repeated the analysis using each country’s local dates. That admitted 2,831 complete-history arrivals across 10 Apple markets and 9 Google Play markets over the full period. Returning shares were 72.92% and 64.73%. In the later local-date sample, the shares were 72.37% and 65.88%, across 7 and 9 markets respectively.

7. Why this matters for ASO competitor research

A busy arrival list does not automatically mean a flood of new competitors. In this sample, much of that activity came from apps returning to a chart they had recently occupied.

For ASO reporting, the useful distinction is between an arrival event, a returning app and a distinct competitor. The chart data can establish the first two and identify distinct app IDs. It cannot establish who competes for the same keywords or users.

This study concerns top-free app charts. It does not measure keyword search ranks, downloads, revenue, advertising or the effectiveness of listing changes. No ASO vendor’s arrival labels were audited, so the report does not claim that a particular tool misclassifies apps.

8. How the study was done

1. Validate the charts. We used 714 valid, complete top-free all-apps snapshots. Each had 100 unique app IDs and every rank from 1 to 100. Paid charts and games charts were excluded.
2. Choose daily observations. Take the latest eligible snapshot for each store, country and UTC date. This produced 671 daily charts over the source period. No missing chart or app rank was filled in.
3. Allow a 14-day history period. Arrival dates were 3 to 26 October. Earlier September observations supplied history. The first comparison period ended 14 October; the later period began 15 October.
4. Identify an arrival. The app must appear in today’s complete top 100 and be absent from yesterday’s complete top 100. The two snapshots must be 18 to 30 hours apart. In the study dates, 414 current daily charts yielded 373 eligible endpoint windows after excluding 29 without a preceding daily chart and 12 with unsuitable timing.
5. Classify the history. Require all 14 preceding daily charts for the main sample. An app seen anywhere in that history is a return. Otherwise it is not observed in that lookback. Incomplete histories remain separately identified.
6. Keep the units clear. The headline is event-weighted because the question concerns arrivals. We separately count distinct app-country combinations, give apps equal weight and retain only first arrivals as sensitivity checks.

The main limits

• Short observation period. The source spans 38 UTC dates. The study measures recent returns, not lifetime first appearances.
• Selected charts and countries. The source is a third-party collection rather than a representative sample of every app or market. Complete-history filtering changes coverage.
• Observed daily returns. We cannot establish continuous absence, detect every intraday crossing or infer why an app disappeared from a chart.
• App identity is not company identity. Two different app IDs can belong to the same publisher. We do not count distinct businesses.
• No causal ASO conclusion. A return can coincide with many changes. The records do not show which changes, if any, caused it.

9. Download the evidence

The evidence ZIP contains the source chart records, snapshot index, metadata used for app names, all arrival classifications, protocol, calculations, verification output and file checksums.

Evidence ZIP: 9.7 MB, embedded in this HTML. Python and NumPy are required to rerun the study. No images or external scripts are needed to read the report.

Source: AppOrbit, pinned data commit. The included source format guide explains chart completeness, date fields and metadata collection. All results were calculated from previously preserved source files; no live storefront census was performed for this experiment.

Publication finding: Most top 100 arrivals in this sample were apps returning to a chart they had occupied within the previous 14 days. Arrival counts therefore describe chart activity more than the number of newly encountered competitors. Tracking stable app IDs and prior appearances changes the interpretation.

Prepared for Gabriel Machuret · ASO Experiment 10 · AppOrbit database rights under ODbL 1.0. Third-party store content retains separate rights. Preserve attribution and consult the included license before redistributing source data.